question stringclasses 91 values | options listlengths 2 4 | answer stringlengths 1 182 | question_type stringclasses 3 values | ts1 listlengths 1.02k 1.08k ⌀ | ts2 listlengths 1.02k 1.2k ⌀ | tid int64 1 102 | difficulty stringclasses 3 values | format_hint stringclasses 1 value | relevant_concepts listlengths 1 8 | question_hint stringclasses 90 values | category stringclasses 5 values | subcategory stringclasses 13 values | id int64 1 746 | ts listlengths 1.02k 1.2k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Which of the given time series has higher variance? | [
"Time Series 2",
"Time Series 1"
] | Time Series 2 | multiple_choice | [
0.0800524205184813,
-0.604205016455626,
-0.5572959803219979,
-1.724270900349981,
-1.0148088601971799,
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-0.04155978338840888,
-0.8714515164516008,
1.008831967735719,
0.22621949577402828,
0.04797012306522118,
-0.43880798675132704,
-0.5569265816939717,
0.03154220515045459,
... | [
-1.9969846760925367,
-0.10029028591418471,
-6.750200287485529,
5.634816115082342,
-2.150451923155779,
1.3977459244594213,
0.7490572464618283,
1.7363236726140696,
-2.260728355618226,
-8.324448232363642,
-4.394954533123961,
-3.9143576497402632,
2.1939438424820206,
-3.9580980526023843,
4.98... | 44 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 501 | null |
You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component? | [
"Time series 2",
"Time series 1"
] | Time series 2 | multiple_choice | [
1.2631885714400095,
-0.13335283592610825,
1.1854915178285637,
1.4408553257635681,
0.41813000539947875,
0.2051941593497593,
-0.44929096700528837,
-0.19325921918451947,
-2.4332074721079553,
-3.042986713967999,
-2.3784157476900583,
-3.0694454216154106,
-1.4804874518388318,
-0.7698499843040822... | [
3.6588646648116017,
-0.6386892520974801,
16.2641080283361,
8.13653947944221,
16.882261424431235,
1.28360131175811,
16.225312862779642,
18.680947664824856,
3.380461299243347,
0.38886974032289157,
-9.633695966226194,
-3.3707479541561733,
-13.120909988256784,
-13.634454196102345,
-5.3794960... | 61 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Variance"
] | The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average? | Noise Understanding | Signal to Noise Ratio Understanding | 502 | null |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"Yes, they both have exponential trend",
"No, time series 1 has linear trend and time series 2 has exponential trend",
"No, time series 1 has exponential trend and time series 2 has log trend"
] | Yes, they both have exponential trend | multiple_choice | [
1.2158672292798718,
1.4604251923738156,
1.9567875639132655,
2.5243984341881887,
2.931785329528459,
3.2906772378449665,
3.519771638396829,
3.5429739102973365,
3.8654815511214706,
3.8512812570178934,
3.612742292614731,
3.653586977073469,
3.406379604264307,
3.0501477791744973,
2.54897024846... | [
-1.4359275660568716,
-1.4488478265870819,
-1.217350616424358,
-0.9791755042193796,
-0.8823605806089211,
-0.5426650673666713,
-0.37429318631203223,
-0.09238086392235922,
-0.16622548714222912,
0.24492054074551578,
0.4377163853998426,
0.4822271037428697,
0.804506284070931,
1.077787689004793,
... | 85 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 503 | null |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Remain the same",
"Decrease",
"Increase"
] | Increase | multiple-choice | null | null | 17 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 504 | [
-0.0698351857452816,
0.3180042599446821,
0.38496241595746306,
0.725858877584475,
1.294779040860491,
1.3772573878932073,
1.6029780601869166,
1.653769382240082,
1.964196645336123,
1.9070900750353001,
1.9817820590874644,
1.7943836381508893,
2.019197021227796,
1.7807581032918411,
1.620930498... |
Is time series 2 a lagged version of time series 1? | [
"No, time series 1 is a lagged version of time series 2",
"No, they do not share similar pattern",
"Yes"
] | No, time series 1 is a lagged version of time series 2 | multiple_choice | [
0.008495356620883602,
0.035658253091154696,
0.017559882770184988,
0.0033848894146013293,
-0.00857853003601682,
-0.003119138861159807,
-0.013117219812014821,
-0.04098674173313611,
-0.032231276082313756,
-0.03871074981763191,
-0.05621825651550269,
-0.06362447833126988,
-0.07788846713232811,
... | [
-0.006450689406810562,
-0.023803098923411148,
-0.021982351590492354,
-0.013811014210772883,
-0.014310677744938423,
-0.010625918955324817,
0.01871472896400398,
-0.010279956883370748,
-0.0034275472754231274,
-0.0029820941595956425,
-0.005132701436774885,
-0.01421245452747309,
-0.01963604594406... | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 505 | null |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"7.08",
"16.72",
"2.37"
] | 7.08 | multiple-choice | null | null | 24 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | After the sine wave, the square wave follows. Begin by identifying where the square wave starts. Next, measure the distance between its peak and baseline. | Pattern Recognition | Cycle Recognition | 506 | [
-0.03116901984535833,
0.5212942318241379,
1.1819866597239999,
1.7875180864438651,
2.0831573375404426,
2.068855969458816,
2.029810511422941,
1.8154284917264005,
1.3811135315668739,
0.9050584497539218,
0.3720726371788709,
-0.2820102921657323,
-0.8699578696569039,
-1.5580355483365307,
-1.80... |
There are two time series given. Is one of them a scaled version of the other? | [
"No, they do not share similar pattern",
"Yes, time series 1 is a scaled version of time series 2",
"Yes, time series 2 is a scaled version of time series 1"
] | Yes, time series 2 is a scaled version of time series 1 | binary | [
-0.09180304574553677,
0.37406240393288437,
0.9606659077184198,
1.2953947171511462,
1.4375733916038724,
2.189477638313527,
2.004182284865782,
2.222287949506576,
2.129003388096686,
2.123984645756905,
1.9133445168309742,
1.6123075075849167,
1.318814337164294,
0.8130466331208311,
0.523583636... | [
-0.0912164710103841,
2.3308113562434,
4.682972019367049,
6.62231953548449,
8.515674829381956,
9.899247177546034,
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10.669897280944415,
9.954536251252561,
8.685448306635788,
6.701162756903884,
4.584197809075872,
2.5213564269308915,... | 86 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Moving Average Process"
] | Scaled version refers to the same pattern but with different amplitude. You should check if the pattern is the same for both time series. If they are the same, you should check the amplitude of the cyclic component. | Similarity Analysis | Shape | 507 | null |
Is the two time series lagged version of each other despite amplitude difference? | [
"Yes, they are lagged versions",
"No, they are not lagged versions"
] | No, they are not lagged versions | binary | [
0,
0.7504174167292271,
1.4318032511124132,
1.9815276890511762,
2.3491707734220957,
2.5011981897039757,
2.424071131094843,
2.125501844170182,
1.6337384197921565,
0.994945155474302,
0.2689214260208186,
-0.47644391930971136,
-1.1714683516388593,
-1.7511377974162892,
-2.1611358357459856,
-... | [
1.137914813543211,
1.2003596134647172,
1.2908136502765188,
1.156882190300832,
0.9921122052630881,
0.9446002401220003,
1.0031925676047069,
0.9896005747936754,
0.9543469696373671,
0.8235783967599117,
0.9630419904382348,
1.035034462847892,
1.1155338444354495,
1.1098910723348878,
1.118877176... | 99 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 508 | null |
The given time series has an increasing trend, is it a linear trend or log trend? | [
"Linear",
"Log"
] | Linear | multiple_choice | null | null | 7 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Log Trend"
] | Check if the slope of the time series is constant or changes over time. | Pattern Recognition | Trend Recognition | 509 | [
0.23414558614026793,
-0.18436763194659048,
-1.110874409631291,
-0.7316569502709479,
0.021702106736065253,
0.15938681096636098,
-0.14956386177014733,
-0.25869105292988037,
0.15530119329471176,
-0.008142037939535843,
-0.1568945717699186,
-0.6246847716362233,
-0.30100969186197996,
0.438971312... |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"6.14",
"2.14",
"15.49"
] | 6.14 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 510 | [
-0.23296309985698863,
1.2271107975728681,
2.313952743028345,
3.4883073378837093,
4.35881047972565,
4.985059021461004,
5.768919567873474,
6.032871491429773,
6.246732172582276,
6.0341267138121335,
5.6870390082443345,
5.047428634040678,
4.181081274679929,
3.543989975013191,
2.34535364773597... |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"1",
"4",
"2"
] | 1 | multiple_choice | null | null | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 511 | [
-0.04424614559435181,
0.002007720092514449,
0.023991125089131524,
0.02838186663355708,
0.037395918685483046,
-0.007585005020634681,
0.025129384207624287,
-0.019292820544840942,
-0.08731484809817808,
0.02852344307218596,
0.018279772290178898,
0.05170830617298726,
0.004244860882038083,
-0.06... |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"High positive autocorrelation around 0.8",
"Negative autocorrelation around -0.8",
"No autocorrelation"
] | No autocorrelation | multiple_choice | null | null | 45 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 512 | [
0.9015817804354014,
-1.039152146012255,
-0.8313454464373976,
-0.5756432107604522,
0.5640752098100851,
-0.8007375730896816,
-0.20520575690396367,
0.022959004378606932,
-0.3556905111373467,
0.9982405062924412,
0.0005284617624184953,
-0.7145257159145092,
-0.5349532874048635,
-0.14819723542084... |
Are the given two time series likely to have the same underlying distribution? | [
"No, they have different underlying distribution",
"Yes, they have the same underlying distribution"
] | Yes, they have the same underlying distribution | binary | [
0.19231667952965611,
-0.1279630369333543,
-0.06032757680493652,
-0.05684955049230508,
-0.0035072174909684325,
0.11555268246105901,
-0.15002003921278628,
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-0.05926435438970846,
-0.3079600605544375,
-0.0751066820369672,
-0.14259558704496245,
0.04425718350070046,
-0.0172... | [
-0.15847793063904014,
0.09188233415777432,
0.1270288327937621,
-0.12930425148079702,
-0.10546986814024126,
0.0010361300039651578,
-0.0778392336935641,
-0.22140135234607025,
0.028647959850817306,
-0.38715613261073795,
-0.415851262092907,
-0.25107770161685605,
-0.3080367845207041,
-0.4286304... | 95 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"AutoRegressive Process",
"Linear Trend"
] | When we say two time series have the same underlying distribution, you should check if they have the same mean and variance. They should also share similar behaviors over time. | Similarity Analysis | Distributional | 513 | null |
Does the following time series exhibit a mean reversion property? | [
"No",
"Yes"
] | Yes | binary | null | null | 46 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean Reversion"
] | Mean reversion first requires the time series have constant mean. You should check this first. Then, see if the time series tends to revert back to the mean after a shock. | Pattern Recognition | AR/MA recognition | 514 | [
-2.5615781849006263,
-8.388616812728449,
-0.6472145011455739,
3.128555404904692,
-14.876076597645717,
1.8927342815950805,
-13.996238705783886,
15.103776187549267,
-20.760877752204216,
21.33195779861674,
-0.3659367303185661,
15.875164786596509,
-16.835098411519766,
21.309964799013112,
-27... |
How does the noise in the given time series influence the detection of periodic pattern in the time series? | [
"No influence, Sinewave",
"Distort the pattern"
] | Distort the pattern | binary | null | null | 58 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Sine Wave",
"Additive Composition"
] | When the noise level is high, it can distort the pattern in the time series. Can you check if you can still detect the cyclic pattern in the time series? | Noise Understanding | Signal to Noise Ratio Understanding | 515 | [
2.2083821807330795,
7.035283327741383,
3.198946850446114,
7.415497120692779,
2.4197819764034927,
3.3316946554543723,
-0.5020195759230837,
6.683855979654127,
7.33321283974416,
-1.7479722590238183,
-2.1474568647400023,
2.4667790123522124,
-2.0543600582989257,
-1.4060066767928814,
1.8444748... |
One type of noise in time series is white noise. Is the given time series noisy (noise dominates other patterns) based on your understanding of white noise? | [
"Yes",
"No"
] | Yes | binary | null | null | 55 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 516 | [
1.5975344292564853,
1.072621307429178,
-0.834367341605201,
-0.5228906364000687,
-0.2917459194429249,
0.26105971872284145,
0.4803222904901281,
-0.5762941348937204,
0.25018893327570446,
-0.7988495680731674,
1.2577636156458918,
1.151002501958202,
-1.7519046544055237,
-0.49964722286667124,
0... |
Piece-wise stationarity means a time series is stationary in distinct segments, with abrupt changes between segments. Each segment has its own constant statistical properties. Does the time series exhibit piecewise stationarity? | [
"No",
"Yes"
] | Yes | binary | null | null | 38 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Gaussian White Noise"
] | Look for segments of the time series that are individually stationary, even if the whole series is not. | Pattern Recognition | Stationarity Detection | 517 | [
3.1918996793300916,
3.130242849597241,
3.0463266202927857,
2.987506454821725,
3.1229624548222295,
3.09245622322855,
3.1409585508743456,
3.1039938094075685,
3.1463226660653287,
2.939296079625432,
3.1015851538623176,
3.2448626005175836,
3.09880591004236,
3.148901412207824,
3.10746875459484... |
Is the two time series lagged version of each other despite amplitude difference? | [
"No, they are not lagged versions",
"Yes, they are lagged versions"
] | No, they are not lagged versions | binary | [
0.02046186446116664,
0.5005413512612464,
0.9376680459951426,
1.336433422660792,
1.671231227316918,
2.0444340387805955,
2.4104932043170524,
2.581701955221137,
2.685725209744167,
2.6429067310267813,
2.5000923301045455,
2.2916045584362807,
1.9715157987559624,
1.5839743677003608,
1.057677369... | [
0.9160396001481887,
0.8507201915160902,
0.7155221299118603,
0.7731551698895467,
0.8307066600083367,
0.895500427129772,
0.7688278308242831,
0.7008662857902476,
0.6372177749266781,
0.6196981885085195,
0.5770751882857375,
0.6886116739256025,
0.6157643507127954,
0.6387456225463443,
0.6864145... | 102 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 518 | null |
The given time series is a gaussian white noise process. What is the most likely noise level (variance)? | [
"9.82",
"6.25",
"1.62"
] | 6.25 | multiple_choice | null | null | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 519 | [
7.683565783435768,
2.761124243872825,
5.891983766594693,
-0.6783872063017069,
5.756356401656944,
0.05346283427943985,
-10.511588339170968,
0.9321038324134571,
0.8688533947669742,
3.018525291785595,
-10.152968602909759,
2.923214919131755,
-6.6582474471461195,
5.817401978039361,
-1.4590624... |
The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series in terms of amplitude? | [
"SquareWave",
"SawtoothWave",
"SineWave"
] | SawtoothWave | multiple-choice | null | null | 20 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition",
"Amplitude"
] | The cyclic patterns have different period and amplitude. The dominant pattern is the one that has the highest amplitude. Identify the pattern with the highest peak. | Pattern Recognition | Cycle Recognition | 520 | [
-4.303577193585501,
-3.802622781639952,
-3.697019620429222,
-3.4395681328216297,
-3.328222879378063,
-3.2101609462301264,
-3.222667399268932,
-3.046081082728167,
-2.9275327495589276,
-2.9472871913939542,
-3.0196151675738108,
-2.9635429651616665,
-2.797345241944912,
-2.827220702802043,
-2... |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is flipped at certain point in time",
"No",
"Yes, it's pattern is distorted by random spikes or noises"
] | Yes, it's pattern is flipped at certain point in time | binary | null | null | 63 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 521 | [
0,
1.0705247455505034,
1.9789280753789844,
2.5878099597891877,
2.8054434545449407,
2.599761854802451,
2.0032484463223095,
1.107983231140512,
0.05160193695998891,
-1.0036916231332424,
-1.8958595012113582,
-2.4877382727884414,
-2.6879546570686195,
-2.464858741849977,
-1.8513501793342901,
... |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is flipped at certain point in time",
"No",
"Yes, it's pattern is distorted by random spikes or noises"
] | Yes, it's pattern is distorted by random spikes or noises | binary | null | null | 63 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 522 | [
0,
-12.030977369941247,
7.780335389894555,
1.9666764407132005,
2.312295241438812,
2.432130239440236,
2.314912352988754,
1.9727430809500466,
-10.479155594980854,
0.769464461704944,
6.833485614783025,
-9.685308807527223,
-1.3732797693502814,
4.649494258409106,
3.5760179822031186,
1.61857... |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"Yes",
"No"
] | No | binary | null | null | 32 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | You can try to identify different parts in the time series first, and see if any part is stationary. | Pattern Recognition | Stationarity Detection | 523 | [
-0.08200046437926264,
-0.15379272521708903,
0.17095895087104213,
0.023417639472294312,
0.22549001115656386,
-0.08911443239274615,
0.18461557731908393,
-0.0020627527770691045,
0.08096145893282984,
0.18052877423981686,
0.030584426880944465,
0.029051470775804736,
0.043909658988671926,
0.03468... |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"4",
"2",
"1"
] | 4 | multiple_choice | null | null | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 524 | [
-0.07030638025129891,
0.1603726889100254,
0.07112310979874366,
0.1307831975469839,
-0.0909503226104608,
-0.18969213795832734,
0.037735326542849765,
0.19247310383743416,
-0.04040407034031099,
0.01932732549561921,
-0.1467916378807842,
0.013998707889709146,
-0.00030452524153777236,
-0.1960939... |
The given time series is a square wave. What is the most likely period of the square wave? | [
"11.87",
"58.11",
"90.96"
] | 11.87 | multiple-choice | null | null | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Square Wave",
"Period"
] | Check the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 525 | [
0.07197447890972958,
1.225727637814599,
1.4496682085198427,
1.536286279637213,
1.4993033114204066,
1.3344772625390677,
-1.236338720546693,
-1.2596126975617241,
-1.4009425653335525,
-1.3877653779100023,
-1.1732489727942041,
-1.3867117221353054,
1.275574968813405,
1.4781807861600407,
1.272... |
The following time series has an anomaly with short term disruption on its pattern. What is the likely pattern of the time series without the anomaly? | [
"Sine wave times linear trend",
"Sawtooth wave times linear trend",
"Square wave times log trend"
] | Sine wave times linear trend | multiple_choice | null | null | 72 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Wander Anomaly"
] | Wander anomaly brings short term disruption on the pattern. You should focus on the overall pattern of the time series without the anomaly. | Anolmaly Detection | General Anomaly Detection | 526 | [
0.04967141530112327,
-0.008310231491890028,
0.0862324178193733,
0.1983905380368468,
0.05320884125624599,
0.0861144468606337,
0.29868983392711923,
0.2429140184692934,
0.13483010216381164,
0.23846304302009314,
0.12463117227792575,
0.09417633143554464,
0.11775039718869412,
-0.1604906837097988... |
Are the given two time series likely to have the same underlying distribution? | [
"No, they have different underlying distribution",
"Yes, they have the same underlying distribution"
] | No, they have different underlying distribution | binary | [
-0.3257038649268941,
0.7672859703852231,
0.9511781588993552,
0.1864763412959544,
-0.048553514796897804,
0.4652688728088133,
1.1908325450992052,
-0.15469757796120326,
-0.8559124340397145,
0.17972729307741064,
0.31533987047228856,
-1.7863379233887877,
-1.2873735489911828,
0.12286097958343545... | [
-0.05219542907864095,
-0.07746727614610241,
0.12022515032992606,
-0.02902097762893166,
0.030595640861460854,
-0.06050309967398223,
-0.13946523205933914,
0.07480020432112242,
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-0.0015768052492970627,
0.10155027101489504,
-0.027481255247304232,
-0.0642063913089986,
-0.047... | 95 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"AutoRegressive Process",
"Linear Trend"
] | When we say two time series have the same underlying distribution, you should check if they have the same mean and variance. They should also share similar behaviors over time. | Similarity Analysis | Distributional | 527 | null |
You are given two AR(1) process, which one of them is more likely to have a larger magnitude in autocorrelation at lag 1? | [
"Time Series 1",
"Time Series 2"
] | Time Series 1 | multiple_choice | [
5.919466965738447,
7.338041429404379,
13.9811233670187,
3.4306637430952733,
-11.518474253015256,
-22.93171342389429,
-6.146192179182355,
-10.930395578454254,
-6.5192413062425185,
-7.869733104807504,
-17.91901589189576,
-20.699139171103482,
-20.5481975701901,
-13.160442504652746,
-14.5429... | [
12.6138084392166,
-2.526946808108262,
1.57434194551466,
-3.55659867071864,
2.5944654443364117,
5.169378268170373,
-8.201125179843583,
-8.847615861483439,
-9.448292676253002,
-1.552318463229627,
-22.2526000528131,
-12.935214710925656,
0.404107117894952,
7.445271370368293,
18.9217964089427... | 47 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation",
"AutoRegressive Process"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. You should compare the two time series to see which one has a larger magnitude in autocorrelation at lag 1. | Pattern Recognition | AR/MA recognition | 528 | null |
You are given two time series which both have a trend component. Do they share the same direction of trend (upward or downward)? | [
"Yes, they have the same direction of trend",
"No, they have different direction of trend"
] | Yes, they have the same direction of trend | binary | [
0.04600275011036025,
0.26858922751421455,
0.495459733696358,
0.7748071117162991,
1.0083692937133182,
1.271152593003562,
1.247983540987218,
1.3531008636851414,
1.5048283131825937,
1.4307777605320768,
1.3559069167112292,
1.171504927844613,
1.2198217264507016,
0.7343062484922094,
0.40982675... | [
0.09275275880357028,
0.2122786501684556,
0.587570066828776,
0.69791030007498,
1.070212657700849,
1.232277803612126,
1.4757310756646513,
1.6143193575358468,
1.9554140465174776,
1.9322991588744203,
2.0631691029033385,
2.1301464499246836,
2.32809369220108,
2.2477755119404965,
2.194563234801... | 81 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave"
] | Trend refers to the general direction of the time series. Are the values going up or down? Check this for both time series to see if they have the same direction of trend. | Similarity Analysis | Shape | 529 | null |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Flip: the pattern is flipped at certain point in time",
"Spike: the pattern of time series is distorted by random large spikes",
"Speed up/down: the period of cyclic components is different from other parts of the time series"
] | Speed up/down: the period of cyclic components is different from other parts of the time series | multiple_choice | null | null | 64 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Spike Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 530 | [
-2.9014286128198323,
-2.7553194569480066,
-2.6092103010761813,
-2.4631011452043556,
-2.31699198933253,
-2.170882833460704,
-2.0247736775888785,
-1.8786645217170532,
-1.7325553658452273,
-1.5864462099734018,
-1.4403370541015763,
-1.2942278982297504,
-1.1481187423579249,
-1.0020095864860994,... |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Exponential -> Linear -> Log",
"Linear -> Exponential",
"Linear -> Exponential -> Log",
"Log"
] | Exponential -> Linear -> Log | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 531 | [
0.977696851740896,
1.1454964173469706,
1.0223789957769274,
1.053362137428456,
1.1031546729903463,
1.1206445886614698,
1.1373921774517959,
1.1882342529527272,
0.9908031512572321,
0.9709396975795452,
1.0271647227049434,
1.006044121462512,
1.1694180455893122,
0.8914141487831149,
1.235791969... |
Despite the noise, does the given two time series have similar pattern? | [
"No, they have different seasonal pattern",
"Yes, they have similar seasonal pattern"
] | Yes, they have similar seasonal pattern | binary | [
-0.2387850012798111,
0.6042988092820634,
1.4753577517794625,
2.0840125711193678,
2.2100669413747442,
2.5457054882950034,
2.352549274917801,
2.765120482832913,
2.7643566625368576,
2.731474277552869,
2.6438151463821216,
2.299880387367086,
1.621445826254675,
1.7108244843797111,
0.8294357645... | [
-0.1778481656456649,
0.5567349701029062,
0.8907996245003125,
1.3292000902495338,
1.188354629160702,
1.4813054901623315,
1.3515646218388075,
1.6808338925744655,
1.9626872966562023,
1.598123290339458,
1.674672445043623,
1.2969773404603948,
1.1308531284589665,
0.5215241179950476,
0.42075650... | 79 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 532 | null |
The time series shows a structural break. What is the most likely cause of this break? | [
"Abrupt frequency change",
"Change in variance in underlying distribution",
"Sudden shift in trend direction"
] | Abrupt frequency change | multiple_choice | null | null | 71 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Gaussian White Noise",
"Sine Wave"
] | You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options. | Anolmaly Detection | General Anomaly Detection | 533 | [
0,
0.3201136734646877,
0.6242592063219617,
0.8972649922445546,
1.1255127601693578,
1.2976168906940388,
1.40499236173694,
1.442282992786174,
1.4076286257501214,
1.3027579147018906,
1.1329020959129386,
0.9065340395610794,
0.634945599923359,
0.33168434697560895,
0.011877776758339056,
-0.3... |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time",
"Speedup: the period of cyclic components is different from other parts of the time series and Cutoff: the pattern of time series disappeared for certain point in t... | Speedup: the period of cyclic components is different from other parts of the time series and Cutoff: the pattern of time series disappeared for certain point in time and became flat | multiple_choice | null | null | 68 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 534 | [
0,
1.1712736999450706,
1.997233234054004,
2.2349216453631677,
1.8157559127255465,
0.865856167840266,
-0.33133853609539116,
-1.4190865586145294,
-2.0730881858738766,
-2.097613785335108,
-1.4835215208265886,
-0.41096715401425277,
0.803997300665648,
1.8031069671234237,
2.2920801354122617,
... |
Is the mean stable over time in the given time series? | [
"No",
"Yes"
] | Yes | binary | null | null | 43 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean"
] | Check if the average value of the time series changes over time. | Pattern Recognition | First Two Moment Recognition | 535 | [
5.556222381276678,
5.537109288963154,
5.577357123364046,
5.615428754579476,
5.551890149496862,
5.596913659172772,
5.513926961884419,
5.574953095020784,
5.627712698936431,
5.6070799758739,
5.605677381823005,
5.647372521788579,
5.651310690683501,
5.697962213958104,
5.699562335451051,
5.5... |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"15.45",
"2.53",
"5.23"
] | 2.53 | multiple-choice | null | null | 24 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | After the sine wave, the square wave follows. Begin by identifying where the square wave starts. Next, measure the distance between its peak and baseline. | Pattern Recognition | Cycle Recognition | 536 | [
0.0001480483909065554,
0.1794033810388137,
0.4464187668493002,
0.759615365641407,
0.8619431980806003,
1.214210506411849,
1.3612174620487807,
1.6642031645348592,
1.4540947029528986,
1.713647761483512,
1.729641283384238,
1.6462005175641143,
1.535166861001492,
1.600716447655432,
1.295755633... |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Flip: the pattern is flipped at certain point in time",
"Spike: the pattern of time series is distorted by random large spikes",
"Speed up/down: the period of cyclic components is different from other parts of the time series"
] | Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 64 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Spike Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 537 | [
-2.9014286128198323,
-2.7553194569480066,
-2.6092103010761813,
-2.4631011452043556,
-2.31699198933253,
-2.170882833460704,
-2.0247736775888785,
-1.8786645217170532,
-1.7325553658452273,
-1.5864462099734018,
-1.4403370541015763,
-1.2942278982297504,
-1.1481187423579249,
-1.0020095864860994,... |
How does the linear trend in the first half of the time series compare to the trend in the second half? | [
"Different",
"Same"
] | Same | binary | null | null | 6 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series is a piecewise linear trend with different slopes in the first and second half. | Pattern Recognition | Trend Recognition | 538 | [
0.014679627776694886,
0.14819040091753158,
-0.22706654887262115,
-0.14912957613961103,
0.00399741483106855,
0.1391416495678601,
-0.033534755612800886,
-0.02834176105708205,
0.058487625037383875,
-0.01776187688141579,
0.19107634441217494,
0.026298428150872962,
0.3278749423073073,
0.03548631... |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Square wave with log trend",
"Sine wave with linear trend",
"Sawtooth wave with exponential trend"
] | Sawtooth wave with exponential trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 539 | [
-0.3697089110510541,
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0.20993059312594597,
0.2679199730400408,
0.32591401607499426,
0.383912732... |
The given time series is a random walk process. What is the most likely noise level (variance) at each step? | [
"1.15",
"16.22"
] | 16.22 | multiple_choice | null | null | 54 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the past value. | Noise Understanding | Red Noise Recognition | 540 | [
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-0.2873247414849876,
-0.1324362791188736... |
You are given two time series following similar pattern. Both of them have an anomaly. Do they have the same type of anomaly? | [
"No. They have different types of anomalies: cutoff and spikes",
"Yes, Time series 1 and time series 2 both have flip anomaly"
] | Yes, Time series 1 and time series 2 both have flip anomaly | binary | [
0,
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1.40... | [
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1.8171... | 76 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Spike Anomaly"
] | For each time series, identify the type of anomaly based on the given definitions. Then, check if they have the same type of anomaly. | Anolmaly Detection | General Anomaly Detection | 541 | null |
Does the trend of the time series change sign or direction at any point? | [
"No",
"Yes"
] | No | binary | null | null | 12 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the overall direction of the time series changes at any point. | Pattern Recognition | Trend Recognition | 542 | [
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0.0... |
Two time series are given. One has noise and the other does not. Do they have similar pattern? | [
"Yes, they are all Sine Wave",
"No, they have different seasonal pattern: Square Wave and Swatooth Wave"
] | No, they have different seasonal pattern: Square Wave and Swatooth Wave | binary | [
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0,
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1.8... | 82 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 543 | null |
Piece-wise stationarity means a time series is stationary in distinct segments, with abrupt changes between segments. Each segment has its own constant statistical properties. Does the time series exhibit piecewise stationarity? | [
"Yes",
"No"
] | No | binary | null | null | 38 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Gaussian White Noise"
] | Look for segments of the time series that are individually stationary, even if the whole series is not. | Pattern Recognition | Stationarity Detection | 544 | [
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-0.011041638242... |
The following time series has a noise component, a trend component, and a cyclic component. Is the noise component more likely to be a white noise or random walk? | [
"Random Walk",
"White Noise"
] | White Noise | binary | null | null | 52 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Gaussian White Noise"
] | White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. This can help you distinguish between white noise and random walk. | Noise Understanding | White Noise Recognition | 545 | [
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2.166352... |
The given time series has a decreasing trend, is it a linear trend or log trend? | [
"Linear",
"Log"
] | Log | multiple_choice | null | null | 8 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Log Trend"
] | Check if the slope of the time series is constant or changes over time. | Pattern Recognition | Trend Recognition | 546 | [
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-0.317270653... |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"2",
"1",
"4"
] | 2 | multiple_choice | null | null | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 547 | [
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0.11... |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Speed up/down: the period of cyclic components is different from other parts of the time series",
"Spike: the pattern of time series is distorted by random large spikes",
"Flip: the pattern is flipped at certain point in time"
] | Speed up/down: the period of cyclic components is different from other parts of the time series | multiple_choice | null | null | 64 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Spike Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 548 | [
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-2.4631011452043556,
-2.31699198933253,
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-1.8786645217170532,
-1.7325553658452273,
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-1.2942278982297504,
-1.1481187423579249,
-1.0020095864860994,... |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Sine wave with linear trend",
"Square wave with log trend",
"Sawtooth wave with exponential trend"
] | Sine wave with linear trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 549 | [
0,
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1.940115789421462,
2.75... |
Both time series have a cyclic components. Which time series has a higher amplitude of the cyclic component? | [
"Time series 1 has higher amplitude",
"Time series 2 has higher amplitude"
] | Time series 2 has higher amplitude | binary | [
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1.521575... | [
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-5.40811919015929... | 83 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 550 | null |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Exponential",
"Linear",
"Log"
] | Exponential | multiple_choice | null | null | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 551 | [
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2.450268506540... |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal",
"No, time series 2 granger causes time series 1"
] | Yes, time series 1 granger causes time series 2 | binary | [
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... | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 552 | null |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"No, they are not granger causal",
"Yes, time series 1 granger causes time series 2"
] | No, time series 2 granger causes time series 1 | binary | [
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... | [
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-0.104670853... | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 553 | null |
The given time series has a decreasing trend, is it a linear trend or log trend? | [
"Log",
"Linear"
] | Log | multiple_choice | null | null | 8 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Log Trend"
] | Check if the slope of the time series is constant or changes over time. | Pattern Recognition | Trend Recognition | 554 | [
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-0.24196288320982767,
-0.32259... |
Covariance stationarity in a time series means constant mean, constant variance and that autocovariance depends only on time lag, not absolute time. Is the given time series covariance-stationary? | [
"Yes",
"No"
] | No | binary | null | null | 36 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"AutoRegressive Process",
"Linear Trend"
] | Check if the covariance between any two points depends only on the time distance between them. | Pattern Recognition | Stationarity Detection | 555 | [
5.369762606765787,
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-2.7576711947... |
What is the most likely mean of the given time series? | [
"6.68",
"-18.84",
"23.16"
] | 6.68 | multiple_choice | null | null | 41 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean"
] | The given time series is stationary. Check the average value of the time series over time. | Pattern Recognition | First Two Moment Recognition | 556 | [
6.635155780502374,
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6.5909494974110086,
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6.715593705611043,
6.... |
You are seeing two instances of random walk. Are they likely to have the same variance? | [
"Yes, they have the same variance",
"No, time series 2 has higher variance",
"No, time series 1 has higher variance"
] | Yes, they have the same variance | multiple_choice | [
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-0.225... | [
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-0.029651621542128262,
0.022339039933032247,
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0.005422639359618779,
-0.008978... | 93 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Variance"
] | Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series. | Similarity Analysis | Distributional | 557 | null |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time",
"Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time",
"Speedup: t... | Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 68 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 558 | [
0,
0.5663879490966458,
1.0537409017463601,
1.3941788417024124,
1.5405573019226617,
1.4731213231960663,
1.2022936642870692,
0.7672037362953696,
0.23016488707819932,
-0.33212048952976814,
-0.8393867109505309,
-1.2191334848045579,
-1.4168585825662534,
-1.4037502708589713,
-1.180753792505165... |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Log",
"Exponential -> Linear -> Log",
"Linear -> Exponential -> Log",
"Linear -> Exponential"
] | Log | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 559 | [
0.0025163665606677756,
-0.017176011593620436,
0.25322889216448835,
0.09052850664538746,
0.04004855163454584,
0.015739920754725766,
0.15701915593479587,
-0.012855634942300145,
-0.050225021897979116,
-0.048384592111445995,
0.22647362789490932,
0.23045666575513873,
0.13097808541237183,
0.0679... |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is flipped at certain point in time",
"No",
"Yes, it's pattern is distorted by random spikes or noises"
] | Yes, it's pattern is flipped at certain point in time | binary | null | null | 63 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 560 | [
0,
1.0374347890157383,
1.9408474960033404,
2.593661210338732,
2.9119185891164294,
2.855210281894561,
2.4319348702072046,
1.698205654474455,
0.7505465879182193,
-0.28667189855782055,
-1.277421747129809,
-2.091723555097594,
-2.622565525290429,
-2.7998328060357975,
-2.5994340887087697,
-2... |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Remain the same",
"Increase",
"Decrease"
] | Remain the same | multiple-choice | null | null | 19 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 561 | [
0.09149505512453714,
0.30378083225821506,
0.7232625954037168,
1.3252432397158602,
1.5950593091847145,
1.9202790427944796,
1.8445886887215541,
1.8524237810429391,
1.9410489524874495,
1.950175989700198,
1.5532403975760516,
1.4036520141993678,
0.9380612980384655,
0.42632239486090767,
0.0106... |
What is the most likely variance of the given time series? | [
"0",
"varies across time",
"0.3"
] | 0 | multiple_choice | null | null | 42 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 562 | [
1,
1.0022116423433387,
1.004428176048532,
1.0066496119335235,
1.0088759608401812,
1.0111072336343518,
1.0133434412059135,
1.015584594468829,
1.0178307043611987,
1.0200817818453143,
1.0223378379077117,
1.0245988835592257,
1.0268649298350427,
1.0291359877947555,
1.031412068522416,
1.0336... |
Is the mean stable over time in the given time series? | [
"No",
"Yes"
] | No | binary | null | null | 43 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean"
] | Check if the average value of the time series changes over time. | Pattern Recognition | First Two Moment Recognition | 563 | [
5.77840936401211,
5.6871795864083206,
5.542927503688082,
5.732404510638226,
5.411140830094437,
5.6598682374906035,
5.679063411882073,
5.759746544415909,
5.728723621201152,
5.721702668340488,
5.589211620963753,
5.627308706143523,
5.512548050863185,
5.617759225259856,
5.788069402788745,
... |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Speed up/down: the period of cyclic components is different from other parts of the time series",
"Spike: the pattern of time series is distorted by random large spikes",
"Flip: the pattern is flipped at certain point in time"
] | Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 64 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Spike Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 564 | [
-2.46398788362281,
-2.3285166784412166,
-2.1930454732596236,
-2.0575742680780302,
-1.9221030628964373,
-1.7866318577148441,
-1.651160652533251,
-1.515689447351658,
-1.3802182421700648,
-1.2447470369884717,
-1.1092758318068785,
-0.9738046266252853,
-0.8383334214436922,
-0.7028622162620992,
... |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Sine wave with linear trend",
"Square wave with log trend",
"Sawtooth wave with exponential trend"
] | Sawtooth wave with exponential trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 565 | [
-0.9444298503238986,
-0.7774608796782927,
-0.6104874917080914,
-0.4435096771292131,
-0.27652742663806285,
-0.10954073091149219,
0.0574504193932428,
0.22444603363852145,
0.3914461212064,
0.5584506914986547,
0.7254597539368206,
0.8924733179622353,
1.0594913930360788,
1.2265139886394172,
1.... |
The given time series has a trend and a cyclic component. It also has an anomaly. What is the most likely combination of components without the anomaly? | [
"Linear trend and sine wave",
"Log trend and sawtooth wave",
"Exponential trend and square wave"
] | Log trend and sawtooth wave | multiple_choice | null | null | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Exponential Trend",
"Square Wave",
"Log Trend",
"Sawtooth Wave",
"Cutoff Anomaly",
"Flip Anomaly"
] | The anomaly only influences a small part of the time series. You should focus on the overall pattern of the time series without the anomaly. Can you recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 566 | [
-1.5175599632000338,
-1.3257101126926678,
-1.1339457485549973,
-0.9422653116169293,
-0.7506672849792972,
-0.5591501924995184,
-0.36771259734446055,
-0.17635310060697462,
0.014929660017240984,
0.2061370114955742,
0.3972702466701732,
0.5883306254181252,
0.7793193757627505,
0.9702376949384527... |
The given time series is a square wave. What is the most likely period of the square wave? | [
"73.94",
"48.26",
"12.89"
] | 73.94 | multiple-choice | null | null | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Square Wave",
"Period"
] | Check the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 567 | [
-0.16224348660170598,
1.5496276520433003,
1.753248634136288,
1.4677678372789507,
1.7008892255011596,
1.6343312256305784,
1.650899665437275,
1.59427125770997,
1.4369000819546365,
1.5766163381281415,
1.5968165869655797,
1.5360082020448027,
1.5019329583736145,
1.4902226491195032,
1.59174205... |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Exponential",
"Log",
"Linear"
] | Exponential | multiple_choice | null | null | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 568 | [
0.9814679276635832,
1.5711613852162196,
1.9119746211985327,
2.4359938662175025,
2.6759460833051536,
3.1654095574696877,
3.4009183233717244,
3.588586613537354,
3.7278015261801465,
3.735653246690929,
3.734931535683004,
3.574067963103117,
3.2905290967783523,
2.9798381408695263,
2.7177655668... |
The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series in terms of amplitude? | [
"SineWave",
"SawtoothWave",
"SquareWave"
] | SineWave | multiple-choice | null | null | 20 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition",
"Amplitude"
] | The cyclic patterns have different period and amplitude. The dominant pattern is the one that has the highest amplitude. Identify the pattern with the highest peak. | Pattern Recognition | Cycle Recognition | 569 | [
0.02322096545566321,
0.48238178946238625,
0.9798975740917453,
1.257779619435488,
1.7543122558249302,
1.98147433620241,
2.4800203390059554,
2.8206023994712477,
2.880952515101414,
3.3742907732016314,
3.7112340120442924,
3.7942559930139903,
3.9800524727942266,
4.104981663312274,
4.443559128... |
The given time series is a square wave. What is the most likely period of the square wave? | [
"85.35",
"14.41",
"40.3"
] | 40.3 | multiple-choice | null | null | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Square Wave",
"Period"
] | Check the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 570 | [
-0.028229685246011535,
2.1061659890224993,
2.3294176089006062,
2.2611341830926834,
2.224257551506335,
2.245866876418883,
2.3055506092186855,
2.376651633449509,
2.208257092861607,
2.2917639089335835,
2.0535261497946116,
2.0924487610203015,
2.055966248243757,
2.284676000158428,
2.222160912... |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"58.56",
"10.98",
"36.44"
] | 10.98 | multiple-choice | null | null | 25 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 571 | [
-2.300373660517314,
-1.8478229066081318,
-1.3927269370707003,
-1.136005258166548,
-0.8656331039262808,
-0.27916938834640176,
0.1195709398815216,
0.45568144333538974,
0.9352283935552713,
1.4898244249858237,
1.897605235686839,
-2.502449498512343,
-2.0479910687660405,
-1.4207593560004177,
-... |
Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity? | [
"No, the variance is different overtime",
"No, the mean is different overtime",
"Yes"
] | No, the variance is different overtime | multiple_choice | null | null | 33 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | For mean, check if the average value changes over time. For variance, check if the degree of variation changes over time. | Pattern Recognition | Stationarity Detection | 572 | [
0.98660980958892,
-0.013941235449263428,
-0.19643004936221897,
-0.5300167268848217,
0.104291155214322,
0.7837248234756866,
0.32935877060062135,
-0.6462678930862157,
-0.316840543729325,
0.0021453455845930197,
0.9000317562716779,
-0.7836617741979502,
0.5837214239543255,
0.42328432328234555,
... |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Decrease",
"Increase",
"Remain the same"
] | Increase | multiple-choice | null | null | 19 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 573 | [
0.04160953130737815,
0.042275195733594534,
0.48079583088133615,
0.6681155324201576,
0.6261005928024288,
0.9383449323677985,
1.2066372496598274,
1.1073151429399066,
1.182155314124875,
1.4382089360243078,
1.1849804048539145,
1.3175403670906911,
1.2316901328311278,
1.2791149615460062,
1.167... |
What type of trend does the time series exhibit in the latter half? | [
"Exponential",
"Linear",
"No trend"
] | No trend | multiple_choice | null | null | 14 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend"
] | Focus on the pattern of growth or decline in the second half of the time series. | Pattern Recognition | Trend Recognition | 574 | [
0.0047825959292708475,
-0.00804963649630625,
-0.00963927416977771,
-0.0013010696429410773,
-0.005033600045045455,
0.007395101750162818,
0.010331604709613168,
-0.0008310950000909936,
0.015807643530615523,
0.02449460500867957,
0.004152634901704529,
0.013982243564184944,
0.015892624361185166,
... |
Is the given time series strictly stationary? | [
"Yes",
"No"
] | No | binary | null | null | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 575 | [
-2.0548381690635056,
-2.071507662468521,
-1.9824952730514376,
-1.7804843139719224,
-1.5753448718392877,
-1.4955693395313707,
-1.211135497178173,
-1.303976988109114,
-1.3052497705307846,
-1.0255411480220054,
-0.8215382383388036,
-0.831080271338082,
-0.7659004576395222,
-0.724125297256778,
... |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Remain the same",
"Increase",
"Decrease"
] | Decrease | multiple-choice | null | null | 19 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 576 | [
-0.10116419648341378,
0.9615982706997565,
2.092295796900004,
2.9642068953562424,
3.9019676199381976,
4.409736009532476,
5.0040822682066155,
5.4079814367279715,
5.743273499714179,
5.584217048660759,
5.413190068059372,
4.923853566641473,
4.70763600306308,
3.9391302770243675,
3.006839740922... |
Is time series 1 a lagged version of time series 2? | [
"No, time series 2 is a lagged version of time series 1",
"No, they do not share similar pattern",
"Yes"
] | No, they do not share similar pattern | multiple_choice | [
0,
0.23614478282205378,
0.4668895685906706,
0.686962589334705,
0.8913454903019434,
1.0753924965101453,
1.234940731966963,
1.3664090719031763,
1.4668831806603404,
1.534184715911321,
1.5669230558806533,
1.5645283212429464,
1.5272649075567328,
1.4562252068907038,
1.3533036677294674,
1.221... | [
-0.2999240565375785,
-0.22414221772570264,
-0.14835638056593714,
-0.07256653706323712,
0.0032273207934276904,
0.07902520103110766,
0.15482711169290286,
0.23063306083799695,
0.30644305654168824,
0.38225710689542225,
0.4580752200068243,
0.5338974039997316,
0.6097236670142256,
0.6855540172066... | 97 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 577 | null |
Which of the following time series is more likely to be an AR(1) process? | [
"Time Series 2",
"Time Series 1"
] | Time Series 2 | multiple_choice | [
0.028511843413565094,
0.025856223918663806,
0.017466376529231536,
0.0027632396049717587,
-0.007833988738855586,
0.02983864174561776,
-0.016261869172086114,
0.02354310080151425,
0.06346643278101759,
0.08548628691100747,
0.06548966399396551,
0.06661166554276003,
0.04641575675515627,
0.018050... | [
3.554449381513181,
0.4925471505008141,
-4.075005565808515,
-10.833702706009085,
-6.6372384933389315,
-4.755480484360465,
-1.385299230240442,
0.07668594148330099,
2.1062025260164434,
12.478542998613307,
9.092139501918428,
10.952751143925632,
3.3548277200102037,
-9.340476973675013,
-7.7784... | 48 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Stationarity"
] | AR(1) process is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. The other option is likely not stationary. | Pattern Recognition | AR/MA recognition | 578 | null |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"18.65",
"7.42",
"1.88"
] | 18.65 | multiple-choice | null | null | 24 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | After the sine wave, the square wave follows. Begin by identifying where the square wave starts. Next, measure the distance between its peak and baseline. | Pattern Recognition | Cycle Recognition | 579 | [
0.29278980067674154,
0.13426253380527647,
0.3876078075666658,
0.5750551693907773,
0.6358078975995153,
0.8420802979496003,
0.9584484819182743,
1.03301044568605,
1.0489095367450259,
1.1471083008242295,
1.0880605998740303,
0.8194928797180412,
1.1736230650219555,
0.7453541244153277,
0.581716... |
Two time series are given. One has noise and the other does not. Do they have similar pattern? | [
"No, they have different seasonal pattern: Square Wave and Swatooth Wave",
"Yes, they are all Sine Wave"
] | No, they have different seasonal pattern: Square Wave and Swatooth Wave | binary | [
-2.5192382946478733,
-2.264301254641273,
-1.9669663369256252,
-1.8146436723971402,
-1.984732828606459,
-1.5268127126622675,
-1.4826213306584166,
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-1.2246443488352035,
-0.8597681339833648,
-0.047688597812414746,
-0.12747587719102782,
-0.5230942810027,
-0.1347530801545969... | [
0,
1.697073993980556,
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1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556,
1.697073993980556... | 82 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 580 | null |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal",
"No, time series 2 granger causes time series 1"
] | No, they are not granger causal | binary | [
0.15934939581558225,
-0.3912701932859606,
-1.0092301733980902,
-1.0887029989083878,
-1.5350241882448852,
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-1.9183410855519434,
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... | [
-0.024185745123089696,
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0.1398656865799581,
0.21975104141746282,
0.15942124000867527,
0.1395197224498086,
0.04533183225258856,
-0.038929286288720395,
-0.08095630377737857,
-0.06138323483069748,
-0.06261936007351039,
-0.097182... | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 581 | null |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution: AR(1)",
"No, they have different underlying distribution: AR(1) and MA(5)"
] | No, they have different underlying distribution: AR(1) and MA(5) | binary | [
21.60658748634631,
29.137199338156364,
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8.111110156254878,
5.223452779105293,
0.053149016215375156,
1.1859051403094363,
-11.442493... | [
9.961003419922635,
9.82867003008755,
10.363126065289437,
9.89305537921424,
8.851968269581569,
9.30345560487375,
9.656946317140074,
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11.079491116049741,
10.356887636405851,
10.210665696763675,
11.916673034480946,
13.718009457049181... | 92 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 582 | null |
The following time series has an anomaly with short term disruption on its pattern. What is the likely pattern of the time series without the anomaly? | [
"Square wave times log trend",
"Sawtooth wave times linear trend",
"Sine wave times linear trend"
] | Square wave times log trend | multiple_choice | null | null | 72 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Wander Anomaly"
] | Wander anomaly brings short term disruption on the pattern. You should focus on the overall pattern of the time series without the anomaly. | Anolmaly Detection | General Anomaly Detection | 583 | [
0.04967141530112327,
0.009850366419908054,
0.11189475528429735,
0.22265463803590146,
0.06994292663125057,
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0.2594997233612216,
0.18067591397091154,
0.2020259589725607,
0.29418708074994043,
0.09986870971514261... |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Linear",
"Exponential",
"Log"
] | Log | multiple_choice | null | null | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 584 | [
0.02664401430657123,
0.49154228139097894,
0.8597050265154107,
1.067691154732312,
1.4274716768700113,
1.8860765227142808,
2.1185510140390145,
2.386888831654937,
2.4222936630772596,
2.8093181212278426,
3.030300002596406,
3.0109041068933835,
3.073861143614937,
3.2494002795241332,
2.98385037... |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"1.71",
"18.13",
"6.95"
] | 18.13 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 585 | [
0.12280904830834792,
2.763508562218093,
5.380056507457005,
8.131303579203356,
10.360198736625218,
12.542388243682106,
14.45138285532635,
15.874368141823878,
17.212072246502263,
17.935057162025444,
18.224817650114645,
18.0495661384605,
17.468929932699083,
16.673955734089887,
15.2425945617... |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"No autocorrelation",
"Negative autocorrelation around -0.8",
"High positive autocorrelation around 0.8"
] | No autocorrelation | multiple_choice | null | null | 45 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 586 | [
-0.8226103028978499,
0.3006696974152848,
-0.1675244967354589,
-1.7663581204810062,
-0.9966325358549638,
-0.8495451018811083,
-0.8258341152989266,
-0.5017705444497561,
-0.25871834182492176,
0.14606880533665892,
0.28641014527705616,
-0.6565143001757547,
-0.8903571599159751,
-0.27479052208761... |
Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude increase over time",
"Amplitude remain the same over time",
"Amplitude decrease over time"
] | Amplitude remain the same over time | multiple-choice | null | null | 28 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 587 | [
-0.013171035631482692,
0.2486920337238431,
0.7185092344494907,
1.139171364601103,
1.5727843799737495,
1.6527922335808185,
2.011832900650435,
2.2775032720236554,
2.47808097374131,
2.672862110725546,
2.5456243429123613,
2.8179195847999385,
2.4826254739884557,
2.5722440426712927,
2.42433188... |
Is time series 1 a lagged version of time series 2? | [
"No, time series 2 is a lagged version of time series 1",
"Yes",
"No, they do not share similar pattern"
] | No, they do not share similar pattern | multiple_choice | [
0,
0.20353811103208183,
0.40088143366222,
0.586033167112795,
0.7533861581004642,
0.8979021074346384,
1.0152725958383255,
1.1020567825492602,
1.1557913758054146,
1.17506936051935,
1.1595849669771408,
1.11014344334638,
1.0286353196604596,
0.9179759858112744,
0.7820125146590324,
0.6254007... | [
-1.6773658201470023,
-1.553152184674679,
-1.4289340816002254,
-1.3047115014806032,
-1.1804844348528152,
-1.0562528722338622,
-0.9320168041207018,
-0.807776220990204,
-0.6835311132991111,
-0.5592814714839935,
-0.4350272859612083,
-0.3107685471268553,
-0.1865052453567353,
-0.062237371006306,... | 97 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 588 | null |
What is the most likely mean of the given time series? | [
"-14.69",
"23.77",
"8.87"
] | 23.77 | multiple_choice | null | null | 41 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean"
] | The given time series is stationary. Check the average value of the time series over time. | Pattern Recognition | First Two Moment Recognition | 589 | [
23.250576792782702,
23.784721387858422,
23.85949242181639,
23.959376668557752,
23.8068237500773,
23.50278249044088,
23.845053523725184,
22.96703961955711,
23.776304452356435,
23.768306104944546,
23.76969026537526,
23.832414551511327,
23.596412459608302,
24.187685620064137,
23.79185013981... |
One type of noise in time series is random walk. Is the given time series noisy (noise dominates other patterns) based on your understanding of random walk | [
"No",
"Yes"
] | No | binary | null | null | 56 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. When the time series has a random walk noise applied to it, it seems like the pattern are even more disrupted. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 590 | [
0,
0.00860237270156675,
0.0172047454031335,
0.02580711810470025,
0.034409490806267,
0.043011863507833745,
0.0516142362094005,
0.06021660891096725,
0.068818981612534,
0.07742135431410074,
0.08602372701566749,
0.09462609971723425,
0.103228472418801,
0.11183084512036774,
0.1204332178219345,... |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Log",
"Exponential",
"Linear"
] | Log | multiple_choice | null | null | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 591 | [
0.01456965719652704,
0.3619558952426082,
0.551034906911333,
0.7473699714526949,
0.9626213223280965,
1.2527730882613661,
1.0775735421788823,
1.3001247351280165,
1.2061481126788303,
1.110793630266814,
1.125749767562582,
0.8407868426603388,
0.542303438005008,
0.4107168485685098,
0.167333997... |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with flip anomaly | multiple_choice | [
-2.135400655639983,
-1.7145959781905489,
-1.2937913007411146,
-0.8729866232916804,
-0.45218194584224625,
-0.03137726839281186,
0.3894274090566221,
0.8102320865060565,
1.2310367639554904,
1.6518414414049247,
2.072646118854359,
-1.7773505149761728,
-1.3565458375267383,
-0.9357411600773043,
... | [
0,
0.5582072831356034,
1.0594466579668202,
1.4526279747475013,
1.6977730343160544,
1.7701274605676274,
1.6627067658851862,
1.387014740584697,
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0.46039432964795357,
-0.09432825933524124,
-0.6348055484802253,
-1.1050026992792916,
-1.4561152759827565,
-1.6515888826063092,... | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 592 | null |
Is time series 1 a lagged version of time series 2? | [
"No, they do not share similar pattern",
"No, time series 2 is a lagged version of time series 1",
"Yes"
] | Yes | multiple_choice | [
0.10999974633522025,
1.4828203805998996,
0.7943383890786301,
1.752017476646276,
-1.9745655395536577,
0.5471282253964695,
1.1572098276381095,
1.0878311942020802,
-1.38035898065553,
0.029902107209757944,
3.2695862701228293,
2.3000744236686606,
0.5345614715620881,
-0.002887363439711299,
-0.... | [
2.4051449869199537,
0.6182677638170895,
0.6026906081533596,
1.5732111687077468,
2.943892102904014,
0.10999974633522025,
1.4828203805998996,
0.7943383890786301,
1.752017476646276,
-1.9745655395536577,
0.5471282253964695,
1.1572098276381095,
1.0878311942020802,
-1.38035898065553,
0.0299021... | 97 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 593 | null |
Two time series are given. Both of them have a noise component. Do they have the same type of noise? | [
"Yes, they both have Gaussian white noise",
"No, they have different noise: white noise and red noise"
] | Yes, they both have Gaussian white noise | binary | [
-1.251503449620389,
-1.0813528349054666,
1.5070877169315922,
0.5032236093532535,
1.7153315793218606,
0.7073996406469085,
-1.1012705513503604,
2.9990107967037596,
0.9494703983343329,
0.05180470348858024,
2.128267183111278,
2.4553873449684698,
2.8299465154653323,
0.9614567816390805,
-0.287... | [
-0.6338569082234912,
0.9497238052580665,
1.4462895671783749,
-0.859743208952998,
3.155835546696119,
1.1485628329875373,
0.20754493749566816,
2.7680241485609844,
2.0083488383460932,
-0.36490338896217045,
1.3685053990827871,
1.5173434407819069,
3.1728142771082455,
1.5614095445211196,
1.446... | 87 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise",
"Additive Composition"
] | When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns. | Similarity Analysis | Shape | 594 | null |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"No",
"Yes"
] | Yes | binary | null | null | 32 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | You can try to identify different parts in the time series first, and see if any part is stationary. | Pattern Recognition | Stationarity Detection | 595 | [
0.5509013962059495,
-0.31200678103989915,
0.19311485900573483,
-0.057528135378963716,
-0.4199079674633141,
-0.46150708347241914,
-0.19696185219831958,
-0.2583559295983047,
0.3759748652562331,
-0.23756985470021955,
0.7372216956248709,
0.25898080006285434,
-0.22318782712949056,
0.20630261002... |
What is the primary cyclic pattern observed in the time series? | [
"No Pattern at all",
"SawtoothWave",
"SineWave",
"SquareWave"
] | SineWave | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 596 | [
0.002242842989120914,
0.2742433491253608,
0.5571767035849391,
0.8124690076887218,
1.067536564536118,
1.2960674853520942,
1.5055851305255485,
1.701774423614828,
1.8613790922881976,
1.9868543331125277,
2.109901961809046,
2.177093869062008,
2.2172328309065925,
2.2324609551629684,
2.19984761... |
Is the given time series likely to have an anomaly? | [
"No",
"Yes, it's pattern is distorted by random spikes or noises",
"Yes, it's pattern is flipped at certain point in time"
] | Yes, it's pattern is distorted by random spikes or noises | binary | null | null | 63 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 597 | [
6.56705229553884,
0.6027992241812874,
-0.7282886707304612,
2.5878099597891877,
2.8054434545449407,
2.599761854802451,
-4.0601985470737585,
8.540840802593422,
-1.4666214474004717,
-1.0036916231332424,
0.5037904054125859,
-13.71144094031937,
-2.6879546570686195,
-2.464858741849977,
-1.8513... |
Does the trend of the time series change sign or direction at any point? | [
"No",
"Yes"
] | No | binary | null | null | 12 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the overall direction of the time series changes at any point. | Pattern Recognition | Trend Recognition | 598 | [
-0.012671414900191073,
-0.022850055478234734,
-0.0034129657999011826,
-0.012454601624181057,
-0.003530238713512566,
-0.018546715277121307,
0.02295182871228088,
0.013103317801358693,
-0.00585884992331134,
0.0017295788466614817,
0.014945964244698035,
0.00535223762388234,
-0.0029639445458177465... |
The given time series is a gaussian white noise process. What is the most likely noise level (variance)? | [
"1.2",
"8.0",
"4.93"
] | 1.2 | multiple_choice | null | null | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 599 | [
-0.2741270712204933,
1.8242735709115097,
0.31741337364671823,
1.4037472521755607,
-2.079056954741752,
0.14651121960112934,
1.1310547616495545,
-0.2674825803366198,
-1.2232698310717285,
-0.2973774333406514,
-0.7145990560949689,
1.6090833844631984,
-0.6314438505578593,
-0.15101975338091594,
... |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Sine wave with linear trend",
"Sawtooth wave with exponential trend",
"Square wave with log trend"
] | Sine wave with linear trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 600 | [
0,
1.1453443323368642,
1.884263490789073,
1.9557369000261544,
1.337532542753327,
0.25416079170202327,
-0.9034474865416535,
-1.717802213196917,
-1.8942173287172202,
-1.3662390756358496,
-0.3194208101370337,
0.8750614470382543,
1.793202704872912,
2.1098630394152624,
1.7150911956471626,
0... |
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