diff --git a/BuildingsBenchComAmy/meta.json b/BuildingsBenchComAmy/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..27fc783d7aa73db6f599712bba5ca14aace1bdc2 --- /dev/null +++ b/BuildingsBenchComAmy/meta.json @@ -0,0 +1,19 @@ +{ + "name": "BuildingsBench900kComstockAmy2018", + "source": "https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench%2F", + "desp": "Simulated building energy consumption datasets, from comstock_amy2018.tar.gz.", + "domain": "Energy", + "license": "Apache", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": [ + "h" + ], + "num_series": 2351, + "num_timesteps": 20592409, + "num_datapoints": 3040599260, + "split": "train", + "quality": "very low", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/BuildingsBenchComAmy/references.bib b/BuildingsBenchComAmy/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..e7c893ffafc51962dc152968e37811a1671368e9 --- /dev/null +++ b/BuildingsBenchComAmy/references.bib @@ -0,0 +1,7 @@ +@inproceedings{Emami2023BuildingsBench, + author = {Emami, Patrick and Sahu, Abhijeet and Graf, Peter}, + title = {BuildingsBench: {A} Large-Scale Dataset of 900k Buildings and Benchmark for Short-Term Load Forecasting}, + booktitle = {Advances in Neural Information Processing Systems 36}, + pages = {19823--19857}, + year = {2023} +} diff --git a/FavoritaTransactionsKnownOil/meta.json b/FavoritaTransactionsKnownOil/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..d803c1fe6f90605d38ae3196b212b09c589af81d --- /dev/null +++ b/FavoritaTransactionsKnownOil/meta.json @@ -0,0 +1,24 @@ +{ + "name": "FavoritaTransactionsKnownOilPrice", + "source": "https://www.kaggle.com/c/favorita-grocery-sales-forecasting/data", + "desp": "In this dataset, you will be predicting the unit sales for thousands of items sold at different Favorita stores located in Ecuador. The training data includes dates, store and item information, whether that item was being promoted, as well as the unit sales. Additional files include supplementary information that may be useful in building your models.In this dataset, we assume oil price is known in advance!", + "domain": "Finance", + "license": "Unknown", + "targets": [ + "transactions" + ], + "covariates": [ + "holiday", + "dcoilwtico" + ], + "timestamp": "date", + "freq": [ + "d" + ], + "num_series": 54, + "num_timesteps": 83488, + "num_datapoints": 250464, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/FavoritaTransactionsKnownOil/references.bib b/FavoritaTransactionsKnownOil/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..f48024912717c7461b030a97543687e65520a81d --- /dev/null +++ b/FavoritaTransactionsKnownOil/references.bib @@ -0,0 +1,6 @@ +@article{favorita-grocery-sales-forecasting, + author = {Corporación Favorita and Inversion and Julia Elliott and Mark McDonald}, + title = {Corporaci{\'o}n Favorita Grocery Sales Forecasting}, + journal = {Kaggle}, + year = {2017} +} diff --git a/GasSensorDynamic/meta.json b/GasSensorDynamic/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..f455d79cfc84e3fccd40e09ff22beb95f853217d --- /dev/null +++ b/GasSensorDynamic/meta.json @@ -0,0 +1,37 @@ +{ + "name": "GasSensorArrayUnderDynamicGasMixtures", + "source": "https://archive.ics.uci.edu/dataset/322/gas+sensor+array+under+dynamic+gas+mixtures", + "desp": "The data set contains the recordings of 16 chemical sensors exposed to two dynamic gas mixtures at varying concentrations. For each mixture, signals were acquired continuously during 12 hours.", + "domain": "Industry", + "license": "CC BY 4.0", + "targets": [ + "sensor3", + "sensor4", + "sensor13", + "sensor11", + "sensor8", + "sensor2", + "sensor1", + "sensor6", + "sensor16", + "sensor10", + "sensor5", + "sensor12", + "sensor9", + "sensor14", + "sensor7", + "sensor15" + ], + "covariates": [ + "Setpoint1", + "Setpoint2" + ], + "timestamp": null, + "freq": null, + "num_series": 2, + "num_timesteps": 2097150, + "num_datapoints": 37748700, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/GasSensorDynamic/references.bib b/GasSensorDynamic/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..34afc028f80c6fc400aeb3fd5be84a01dd008824 --- /dev/null +++ b/GasSensorDynamic/references.bib @@ -0,0 +1,6 @@ +@article{gas_sensor_array_under_dynamic_gas_mixtures_322, + author = {Fonollosa, Jordi}, + title = {Gas Sensor Array under Dynamic Gas Mixtures}, + journal = {UCI Machine Learning Repository}, + year = {2015} +} diff --git a/MZVAV/meta.json b/MZVAV/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..5afedb12083cb8f9bd9126121c18b8679f93f1bf --- /dev/null +++ b/MZVAV/meta.json @@ -0,0 +1,17 @@ +{ + "name": "MZVAV", + "source": "https://figshare.com/articles/dataset/LBNLDataSynthesisInventory_pdf/11752740", + "desp": "This data set can be used to evaluate and benchmark the performance accuracy of FDD algorithms or tools. It contains operational data from physical experimentation as well as simulation. The data sets currently cover AHU-VAV systems and RTUs, and will be expanded over time to include additional building systems and fault conditions. They include data from commonly available measurement points, spanning system operations under a variety of fault-present and fault-free conditions. This operational data is paired with ground truth information as to which faults are present during which time periods.", + "domain": "Others", + "license": "CC0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 6, + "num_timesteps": 398879, + "num_datapoints": 6832789, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/MZVAV/references.bib b/MZVAV/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..0900973fdd495a5a80adef00a906cf68aa7c376c --- /dev/null +++ b/MZVAV/references.bib @@ -0,0 +1,6 @@ +@article{Granderson2020, + author = {Jessica Granderson and Guanjing Lin and Ari Harding and Piljae Im and Yan Chen}, + title = {Dataset for building fault detection and diagnostics algorithm creation and performance testing}, + journal = {Figshare}, + year = {2020} +} \ No newline at end of file diff --git a/Mdense/references.bib b/Mdense/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..5658bff979001fca4a6a9d800651a055edc14762 --- /dev/null +++ b/Mdense/references.bib @@ -0,0 +1,7 @@ +@article{de2020spatio, + title={A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction}, + author={de Medrano, Rodrigo and Aznarte, Jose L}, + journal={Applied Soft Computing}, + volume={96}, + year={2020} +} \ No newline at end of file diff --git a/MelbournePedestrianCounts/meta.json b/MelbournePedestrianCounts/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..487aaf95fc6c4f1707320372bd66d272f80a601c --- /dev/null +++ b/MelbournePedestrianCounts/meta.json @@ -0,0 +1,19 @@ +{ + "name": "MelbournePedestrianCounts", + "source": "https://zenodo.org/records/4656626", + "desp": "This dataset contains hourly pedestrian counts captured from 66 sensors in Melbourne city starting from May 2009.", + "domain": "Others", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "h" + ], + "num_series": 66, + "num_timesteps": 3132346, + "num_datapoints": 3132346, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/MelbournePedestrianCounts/references.bib b/MelbournePedestrianCounts/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..81102ab033e8cd1e872f9b14de54f8f93bd9b0df --- /dev/null +++ b/MelbournePedestrianCounts/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2020_4656626, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Melbourne Pedestrian Counts Dataset}, + journal = {Zenodo}, + year = {2020} +} diff --git a/MetroTraffic/meta.json b/MetroTraffic/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..c021e90555fdaa9502fb477ca51ec65710a43547 --- /dev/null +++ b/MetroTraffic/meta.json @@ -0,0 +1,24 @@ +{ + "name": "MetroTraffic", + "source": "https://archive.ics.uci.edu/dataset/492/metro+interstate+traffic+volume", + "desp": "Hourly Minneapolis-St Paul, MN traffic volume for westbound I-94.Includes weather and holiday features from 2012-2018.", + "domain": "Traffic", + "license": "CC BY 4.0", + "targets": [ + "traffic_volume" + ], + "covariates": [ + "clouds_all", + "rain_1h", + "temp", + "snow_1h" + ], + "timestamp": "date_time", + "freq": null, + "num_series": 1, + "num_timesteps": 48204, + "num_datapoints": 241020, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/MetroTraffic/references.bib b/MetroTraffic/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..b1d89ed013300a43acf45bf880427a6c9ebaee76 --- /dev/null +++ b/MetroTraffic/references.bib @@ -0,0 +1,6 @@ +@article{metro_interstate_traffic_volume_492, + author = {Hogue, John}, + title = {Metro Interstate Traffic Volume}, + journal = {UCI Machine Learning Repository}, + year = {2019} +} diff --git a/Metropt3/meta.json b/Metropt3/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..6295f070ae0fcce42d0216c031fef4bc53f23e31 --- /dev/null +++ b/Metropt3/meta.json @@ -0,0 +1,34 @@ +{ + "name": "Metropt3", + "source": "https://archive.ics.uci.edu/dataset/791/metropt+3+dataset", + "desp": "From a metro train in an operational context, readings from pressure, temperature, motor current, and air intake valves were collected from a compressor's Air Production Unit (APU). This dataset reveals real predictive maintenance challenges encountered in the industry. It can be used for failure predictions, anomaly explanations, and other tasks.", + "domain": "Traffic", + "license": "CC BY 4.0", + "targets": [ + "DV_pressure", + "Motor_current", + "Oil_temperature", + "TP2", + "TP3", + "Reservoirs", + "H1" + ], + "covariates": [ + "COMP", + "DV_eletric", + "Towers", + "MPG", + "LPS", + "Pressure_switch", + "Oil_level", + "Caudal_impulses" + ], + "timestamp": null, + "freq": null, + "num_series": 1, + "num_timesteps": 1048575, + "num_datapoints": 15728625, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/Metropt3/references.bib b/Metropt3/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..23b45540ca1323d07de1c6f66de1a7797d1795f3 --- /dev/null +++ b/Metropt3/references.bib @@ -0,0 +1,6 @@ +@article{metropt-3_dataset_791, + author = {Davari, Narjes and Veloso, Bruno and Ribeiro, Rita and Gama, Joao}, + title = {MetroPT-3 Dataset}, + journal = {UCI Machine Learning Repository}, + year = {2021} +} diff --git a/MiniApp/meta.json b/MiniApp/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..5496fb80273ceb1e398553cba831eeb70577ead1 --- /dev/null +++ b/MiniApp/meta.json @@ -0,0 +1,49 @@ +{ + "name": "MiniApp", + "source": "https://github.com/zhanxingzhu/Functional_Relation_Field_Time_Series", + "desp": "Real-word flow data from two popular online payment MiniApps.", + "domain": "Others", + "license": "GPL-3.0", + "targets": [ + "f2", + "f16", + "f1", + "f11", + "f19", + "f7", + "f8", + "f20", + "f17", + "f12", + "f18", + "f14", + "f10", + "f13", + "f15", + "f5", + "f4", + "f6", + "f9", + "f3" + ], + "covariates": [ + "f21", + "f30", + "f25", + "f28", + "f27", + "f24", + "f22", + "f29", + "f26", + "f23" + ], + "timestamp": null, + "freq": null, + "num_series": 2, + "num_timesteps": 12960, + "num_datapoints": 340416, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/MiniApp/references.bib b/MiniApp/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..d651750aa7a4a429e02d63d878c952cf2ba0cbcf --- /dev/null +++ b/MiniApp/references.bib @@ -0,0 +1,7 @@ +@article{li2024functional, + title={Functional Relation Field: {A} Model-Agnostic Framework for Multivariate Time Series Forecasting}, + author={Li, Ting and Yu, Bing and Li, Jianguo and Zhu, Zhanxing}, + journal={Artificial Intelligence}, + volume={334}, + year={2024} +} \ No newline at end of file diff --git a/MotionSense/meta.json b/MotionSense/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..2585efe29367ade5148ebf309977c5eaed1ab6ca --- /dev/null +++ b/MotionSense/meta.json @@ -0,0 +1,21 @@ +{ + "name": "MotionSense", + "source": "https://github.com/mmalekzadeh/motion-sense", + "desp": "This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using SensingKit which collects information from Core Motion framework on iOS devices. All data collected in 50Hz sample rate. A total of 24 participants in a range of gender, age, weight, and height performed 6 activities in 15 trials in the same environment and conditions: downstairs, upstairs, walking, jogging, sitting, and standing.", + "domain": "Others", + "license": "MIT", + "targets": [ + "x", + "y", + "z" + ], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 572, + "num_timesteps": 2474372, + "num_datapoints": 7423116, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/MotionSense/references.bib b/MotionSense/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..2d372c185cb538e6e90e5fa44d23fe5abc42c9f1 --- /dev/null +++ b/MotionSense/references.bib @@ -0,0 +1,7 @@ +@inproceedings{Malekzadeh:2019:MSD:3302505.3310068, + author = {Malekzadeh, Mohammad and Clegg, Richard G. and Cavallaro, Andrea and Haddadi, Hamed}, + title = {Mobile Sensor Data Anonymization}, + booktitle = {Proceedings of the 2019 International Conference on Internet of Things Design and Implementation}, + year = {2019}, + pages = {49--58} +} \ No newline at end of file diff --git a/MotorTemperature/meta.json b/MotorTemperature/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..5b1229613fa70bcca94c44df72923474112d0da8 --- /dev/null +++ b/MotorTemperature/meta.json @@ -0,0 +1,31 @@ +{ + "name": "MotorTemperature", + "source": "https://www.kaggle.com/datasets/wkirgsn/electric-motor-temperature", + "desp": "The data set comprises several sensor data collected from a permanent magnet synchronous motor (PMSM) deployed on a test bench. The PMSM represents a german OEM's prototype model. Test bench measurements were collected by the LEA department at Paderborn University.", + "domain": "Others", + "license": "CC BY-SA 4.0", + "targets": [ + "pm", + "stator_winding", + "stator_yoke", + "coolant", + "stator_tooth" + ], + "covariates": [ + "torque", + "motor_speed", + "ambient", + "i_d", + "i_q", + "u_d", + "u_q" + ], + "timestamp": null, + "freq": null, + "num_series": 69, + "num_timesteps": 1330816, + "num_datapoints": 15969792, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/MotorTemperature/references.bib b/MotorTemperature/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..7b4e632978d4b167459c0af54acecb0942a4fc08 --- /dev/null +++ b/MotorTemperature/references.bib @@ -0,0 +1,6 @@ +@article{wilhelm_kirchgassner_2021, + author = {Wilhelm Kirchgässner and Oliver Wallscheid and Joachim B{\"o}cker}, + title = {Electric Motor Temperature}, + journal = {Kaggle}, + year = {2021} +} diff --git a/NAB/meta.json b/NAB/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0f03d124288c3095e3f93eafd5ef4d2ff09856 --- /dev/null +++ b/NAB/meta.json @@ -0,0 +1,19 @@ +{ + "name": "NAB", + "source": "https://github.com/numenta/NAB?ref=hackernoon.com", + "desp": "This repository contains the data and scripts which comprise the Numenta Anomaly Benchmark (NAB) v1.1. NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is composed of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications.", + "domain": "Others", + "license": "MIT", + "targets": [ + "value" + ], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 47, + "num_timesteps": 321206, + "num_datapoints": 321206, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/NAB/references.bib b/NAB/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..37d924d43f84b4952b28aa83f2ea437ff47418d1 --- /dev/null +++ b/NAB/references.bib @@ -0,0 +1,6 @@ +@article{ahmad2017unsupervised, + author = {Ahmad, Subutai and Lavin, Alexander and Purdy, Scott and Agha, Zuha}, + title = {Unsupervised real-time anomaly detection for streaming data}, + journal = {Neurocomputing}, + year = {2017} +} diff --git a/NIFTYStock/meta.json b/NIFTYStock/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..27bb638d991a78fce3f672ead112cd7c32f9f4d6 --- /dev/null +++ b/NIFTYStock/meta.json @@ -0,0 +1,28 @@ +{ + "name": "NIFTYStock", + "source": "https://www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data", + "desp": "Stock price data of the fifty stocks in NIFTY-50 index from NSE India. The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All datasets are at a day-level with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks itself.", + "domain": "Finance", + "license": "CC0", + "targets": [ + "Last", + "VWAP", + "Adj Close", + "Prev Close", + "Close", + "Volume", + "Low", + "High", + "Open", + "Turnover" + ], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 52, + "num_timesteps": 472259, + "num_datapoints": 4244406, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/NIFTYStockKnownOpen/meta.json b/NIFTYStockKnownOpen/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..4287b7daf16afb35d8deea073b013b70475b459e --- /dev/null +++ b/NIFTYStockKnownOpen/meta.json @@ -0,0 +1,29 @@ +{ + "name": "NIFTYStockKnownOpen", + "source": "https://www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data", + "desp": "Stock price data of the fifty stocks in NIFTY-50 index from NSE India. The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All datasets are at a day-level with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks itself. Assume open price is known.", + "domain": "Finance", + "license": "CC0", + "targets": [ + "Last", + "VWAP", + "Adj Close", + "Prev Close", + "Close", + "Volume", + "Low", + "High", + "Turnover" + ], + "covariates": [ + "Open" + ], + "timestamp": null, + "freq": null, + "num_series": 51, + "num_timesteps": 472209, + "num_datapoints": 4244406, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/NN5Daily/meta.json b/NN5Daily/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..865b1413be189d14bd9a6de903fcc2c830077d51 --- /dev/null +++ b/NN5Daily/meta.json @@ -0,0 +1,19 @@ +{ + "name": "NN5Daily", + "source": "https://zenodo.org/records/4656110", + "desp": "This dataset was used in the NN5 forecasting competition. It contains 111 daily time series from the banking domain. The goal is predicting the daily cash withdrawals from ATMs in UK.", + "domain": "Finance", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "d" + ], + "num_series": 1, + "num_timesteps": 791, + "num_datapoints": 87801, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/NN5Daily/references.bib b/NN5Daily/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..fd13b9336614f0d7aadfe6e36361b167333712b7 --- /dev/null +++ b/NN5Daily/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2020_4656110, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {NN5 Daily Dataset (with Missing Values)}, + journal = {Zenodo}, + year = {2020} +} diff --git a/OPSD-Household/meta.json b/OPSD-Household/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..09d1805020c6412ee8057271e62a3a5147c52043 --- /dev/null +++ b/OPSD-Household/meta.json @@ -0,0 +1,21 @@ +{ + "name": "OPSD-Household", + "source": "https://data.open-power-system-data.org/household_data/2020-04-15", + "desp": "This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. ", + "domain": "Energy", + "license": "MIT", + "targets": [], + "covariates": [], + "timestamp": "utc_timestamp", + "freq": [ + "15min", + "min", + "h" + ], + "num_series": 33, + "num_timesteps": 7121240, + "num_datapoints": 47880484, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/OPSD-PV-Wind/meta.json b/OPSD-PV-Wind/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..1b614e75742fa991d931a6f1d0051c9ce905da0e --- /dev/null +++ b/OPSD-PV-Wind/meta.json @@ -0,0 +1,19 @@ +{ + "name": "OPSD-PV-Wind", + "source": "https://data.open-power-system-data.org/ninja_pv_wind_profiles/2020-09-16", + "desp": "This data package contains simulated wind and PV capacity factors from Renewables.ninja, at hourly resolution, for all European countries. Unlike the time series data package, which contains data reported from network operators, this package contains simulated data using historical weather conditions.", + "domain": "Energy", + "license": "CC BY-NC 4.0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": [ + "h" + ], + "num_series": 36, + "num_timesteps": 12623040, + "num_datapoints": 48738960, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/OPSD-PV-Wind/references.bib b/OPSD-PV-Wind/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..b924c95be7d303b4e10e3d795d3e8a8c05efe8b1 --- /dev/null +++ b/OPSD-PV-Wind/references.bib @@ -0,0 +1,17 @@ +@article{Pfenninger2016PV, + author = {Pfenninger, Stefan and Staffell, Iain}, + title = {Long-term Patterns of European {PV} Output Using 30 years of Validated Hourly Reanalysis and Satellite Data}, + journal = {Energy}, + volume = {114}, + pages = {1251--1265}, + year = {2016} +} + +@article{Staffell2016Wind, + author = {Staffell, Iain and Pfenninger, Stefan}, + title = {Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output}, + journal = {Energy}, + volume = {114}, + pages = {1224--1239}, + year = {2016} +} diff --git a/OPSD-When2Heat/meta.json b/OPSD-When2Heat/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..1d484e2f280cb60361d2401c52501babafee0669 --- /dev/null +++ b/OPSD-When2Heat/meta.json @@ -0,0 +1,19 @@ +{ + "name": "OPSD-When2Heat", + "source": "https://data.open-power-system-data.org/when2heat/2023-07-27", + "desp": "This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for 28 European countries from 2008 to 2022 in an hourly resolution. Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2015, the data is additionally scaled with annual statistics on the final energy consumption for heating. COP time series for different heat sources �C air, ground, and groundwater using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements. ", + "domain": "Energy", + "license": "CC BY-NC 4.0", + "targets": [], + "covariates": [], + "timestamp": "utc_timestamp", + "freq": [ + "h" + ], + "num_series": 28, + "num_timesteps": 2086138, + "num_datapoints": 45614480, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/OPSD-When2Heat/references.bib b/OPSD-When2Heat/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..b3da11ff673607554fce4d955913c64e976e93ab --- /dev/null +++ b/OPSD-When2Heat/references.bib @@ -0,0 +1,8 @@ +@article{ruhnau2019heatdemand, + author = {Ruhnau, Oliver and Hirth, Lion and Praktiknjo, Aaron}, + title = {Time series of heat demand and heat pump efficiency for energy system modeling}, + journal = {Scientific Data}, + volume = {6}, + pages = {189}, + year = {2019} +} diff --git a/OPSD/meta.json b/OPSD/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..34948c013d8a111ce0a2e9ec86b863b76970b22f --- /dev/null +++ b/OPSD/meta.json @@ -0,0 +1,19 @@ +{ + "name": "OPSD", + "source": "https://data.open-power-system-data.org/", + "desp": "This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See previous versions for historical data from a broader range of sources.", + "domain": "Energy", + "license": "MIT", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": [ + "h" + ], + "num_series": 80, + "num_timesteps": 2859861, + "num_datapoints": 22899412, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/OccupancyDetection/meta.json b/OccupancyDetection/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..eb05398c6eabd9f81e0bea9ab4d3ec4a94c4bf71 --- /dev/null +++ b/OccupancyDetection/meta.json @@ -0,0 +1,25 @@ +{ + "name": "OccupancyDetection", + "source": "https://archive.ics.uci.edu/dataset/357/occupancy+detection", + "desp": "Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute.", + "domain": "Industry", + "license": "CC BY 4.0", + "targets": [ + "CO2", + "Temperature" + ], + "covariates": [ + "Occupancy", + "Light", + "Humidity", + "HumidityRatio" + ], + "timestamp": "date", + "freq": null, + "num_series": 3, + "num_timesteps": 20560, + "num_datapoints": 123360, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/OccupancyDetection/references.bib b/OccupancyDetection/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..0fcb38f07754b52b8234040fec1ebbeed16b834d --- /dev/null +++ b/OccupancyDetection/references.bib @@ -0,0 +1,6 @@ +@article{occupancy_detection__357, + author = {Candanedo, Luis}, + title = {Occupancy Detection}, + journal = {UCI Machine Learning Repository}, + year = {2016} +} diff --git a/OikolabWeather/meta.json b/OikolabWeather/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e7304c10ef7058bb47ea04146b6400edc68ea7e0 --- /dev/null +++ b/OikolabWeather/meta.json @@ -0,0 +1,28 @@ +{ + "name": "OikolabWeather", + "source": "https://zenodo.org/records/5184708", + "desp": "This dataset was kindly provided by OikoLab (https://oikolab.com). It contains eight time series representing the hourly climate data nearby Monash University, Clayton, Victoria, Australia from 2010-01-01 to 2021-05-31. The climate data include temperature (C), dewpoint temperature (C), wind speed (m/s), mean sea level pressure (Pa), relative humidity (0-1), surface solar radiation (W/m^2), surface thermal radiation (W/m^2) and total cloud cover (0-1).", + "domain": "Environment", + "license": "CC BY 4.0", + "targets": [ + "mean_sea_level_pressure", + "temperature", + "surface_solar_radiation", + "dewpoint_temperature", + "relative_humidity", + "total_cloud_cover", + "surface_thermal_radiation", + "wind_speed" + ], + "covariates": [], + "timestamp": null, + "freq": [ + "h" + ], + "num_series": 1, + "num_timesteps": 100057, + "num_datapoints": 800456, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/OikolabWeather/references.bib b/OikolabWeather/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..00a1d4985b88538987f752c0bf0545c01a16aa27 --- /dev/null +++ b/OikolabWeather/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2021_5184708, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Oikolab Weather Dataset}, + journal = {Zenodo}, + year = {2021} +} diff --git a/OilWell/meta.json b/OilWell/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..d5d844480a2db9a517f57ddf3e14768288da4c17 --- /dev/null +++ b/OilWell/meta.json @@ -0,0 +1,17 @@ +{ + "name": "OilWell", + "source": "https://www.kaggle.com/datasets/afrniomelo/3w-dataset", + "desp": "A realistic and public dataset with rare undesirable real events in oil wells.", + "domain": "Energy", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 1984, + "num_timesteps": 50913215, + "num_datapoints": 244525350, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/OilWell/references.bib b/OilWell/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..5059b5bc66411ef5d628e96b16c9411445ad9a91 --- /dev/null +++ b/OilWell/references.bib @@ -0,0 +1,7 @@ +@article{vargas2019realistic, + title={A realistic and public dataset with rare undesirable real events in oil wells}, + author={Vargas, Ricardo Emanuel Vaz and Munaro, Celso Jos{\'e} and Ciarelli, Patrick Marques and Medeiros, Andr{\'e} Gon{\c{c}}alves and do Amaral, Bruno Guberfain and Barrionuevo, Daniel Centurion and de Ara{\'u}jo, Jean Carlos Dias and Ribeiro, Jorge Lins and Magalh{\~a}es, Lucas Pierezan}, + journal={Journal of Petroleum Science and Engineering}, + volume={181}, + year={2019} +} \ No newline at end of file diff --git a/PAMAP2/meta.json b/PAMAP2/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..5bae56f250684acee6110b145679b921c9543627 --- /dev/null +++ b/PAMAP2/meta.json @@ -0,0 +1,62 @@ +{ + "name": "PAMAP2", + "source": "https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring", + "desp": "The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities (such as walking, cycling, playing soccer, etc.), performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. The dataset can be used for activity recognition and intensity estimation, while developing and applying algorithms of data processing, segmentation, feature extraction and classification.", + "domain": "Others", + "license": "CC BY 4.0", + "targets": [ + "accelerometer_16_y", + "temperature.1", + "accelerometer_6_y.1", + "accelerometer_16_x", + "accelerometer_16_y.1", + "magnetometer_x.2", + "gyroscope_z.2", + "accelerometer_6_y.2", + "accelerometer_16_z.2", + "magnetometer_x.1", + "accelerometer_6_z.2", + "magnetometer_z", + "gyroscope_x.2", + "temperature.2", + "gyroscope_y", + "magnetometer_y.1", + "magnetometer_x", + "accelerometer_6_y", + "accelerometer_16_y.2", + "magnetometer_y", + "gyroscope_z", + "magnetometer_z.1", + "accelerometer_16_z.1", + "accelerometer_6_x.1", + "gyroscope_x", + "accelerometer_6_x", + "gyroscope_y.1", + "accelerometer_16_x.2", + "magnetometer_y.2", + "gyroscope_z.1", + "heartrate", + "accelerometer_16_x.1", + "accelerometer_16_z", + "accelerometer_6_z", + "magnetometer_z.2", + "accelerometer_6_x.2", + "gyroscope_x.1", + "temperature", + "accelerometer_6_z.1", + "gyroscope_y.2" + ], + "covariates": [ + "activity" + ], + "timestamp": null, + "freq": [ + "10ms" + ], + "num_series": 9, + "num_timesteps": 2724953, + "num_datapoints": 111723073, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/PAMAP2/references.bib b/PAMAP2/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..645203cc14a1f89f5ce0667b16e99a27382061e1 --- /dev/null +++ b/PAMAP2/references.bib @@ -0,0 +1,6 @@ +@article{reiss2012pamap2, + author = {Reiss, Attila}, + title = {PAMAP2 Physical Activity Monitoring}, + journal = {UCI Machine Learning Repository}, + year = {2012} +} \ No newline at end of file diff --git a/PEMS-Bay-METRO-LA/meta.json b/PEMS-Bay-METRO-LA/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..875084ae077a58750113058abc22112172d93310 --- /dev/null +++ b/PEMS-Bay-METRO-LA/meta.json @@ -0,0 +1,19 @@ +{ + "name": "PEMS-Bay-METRO-LA", + "source": "https://github.com/liyaguang/DCRNN", + "desp": "The traffic flows for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY).", + "domain": "Traffic", + "license": "MIT", + "targets": [], + "covariates": [], + "timestamp": "timestamp", + "freq": [ + "5min" + ], + "num_series": 2, + "num_timesteps": 86388, + "num_datapoints": 24032004, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/PEMS-Bay-METRO-LA/references.bib b/PEMS-Bay-METRO-LA/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..fa04a4f0b522a490e33afbc667e6ba3766847610 --- /dev/null +++ b/PEMS-Bay-METRO-LA/references.bib @@ -0,0 +1,6 @@ +@inproceedings{li2018dcrnn_traffic, + title={Diffusion Convolutional Recurrent Neural Network: {D}ata-Driven Traffic Forecasting}, + author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan}, + booktitle={Proceedings of the 2018 International Conference on Learning Representations}, + year={2018} +} \ No newline at end of file diff --git a/PEMSCalifornia/meta.json b/PEMSCalifornia/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..d28c5a27d74c9afaf7a0bdf2aa47da7fc2d9d310 --- /dev/null +++ b/PEMSCalifornia/meta.json @@ -0,0 +1,17 @@ +{ + "name": "PEMSCalifornia", + "source": "https://github.com/LibCity/Bigscity-LibCity-Datasets", + "desp": "Traffic flows in California cities.", + "domain": "Traffic", + "license": "Apache-2.0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 8, + "num_timesteps": 105726, + "num_datapoints": 38220894, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/PEMSCalifornia/references.bib b/PEMSCalifornia/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..5f5d20643af002044e1729541e434f500cb65546 --- /dev/null +++ b/PEMSCalifornia/references.bib @@ -0,0 +1,7 @@ +@inproceedings{10.1145/3474717.3483923, + author = {Wang, Jingyuan and Jiang, Jiawei and Jiang, Wenjun and Li, Chao and Zhao, Wayne Xin}, + title = {LibCity: {A}n Open Library for Traffic Prediction}, + year = {2021}, + booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems}, + pages = {145--148}, +} \ No newline at end of file diff --git a/PM25FiveCities/meta.json b/PM25FiveCities/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..1fd945577cd78d093caac52a593079371e457038 --- /dev/null +++ b/PM25FiveCities/meta.json @@ -0,0 +1,19 @@ +{ + "name": "PM25FiveCities", + "source": "https://archive.ics.uci.edu/dataset/394/pm2+5+data+of+five+chinese+cities", + "desp": "This hourly data set contains the PM2.5 data in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang. Meanwhile, meteorological data for each city are also included.", + "domain": "Environment", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "timestamp", + "freq": [ + "h" + ], + "num_series": 5, + "num_timesteps": 112920, + "num_datapoints": 1151784, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/PM25FiveCities/references.bib b/PM25FiveCities/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..86e0edc4e2ed079af7bbc46abc5bfff3719fda9f --- /dev/null +++ b/PM25FiveCities/references.bib @@ -0,0 +1,6 @@ +@article{chen2016pm25, + author = {Chen, Song}, + title = {PM2.5 Data of Five Chinese Cities}, + journal = {UCI Machine Learning Repository}, + year = {2016} +} diff --git a/PUMP/meta.json b/PUMP/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..4d416ce2f116b09279ae00f8ee93b594ffa7a34b --- /dev/null +++ b/PUMP/meta.json @@ -0,0 +1,17 @@ +{ + "name": "PUMP", + "source": "https://www.kaggle.com/datasets/nphantawee/pump-sensor-data", + "desp": "Sensor readings from a water pump of a small area far from big town, there are 7 system failure in test dataset.", + "domain": "Industry", + "license": "Unknown", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 2, + "num_timesteps": 220302, + "num_datapoints": 9693288, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/ProEnFo/meta.json b/ProEnFo/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..891ed2220134ed3dafb18717f6b96bc497b6a174 --- /dev/null +++ b/ProEnFo/meta.json @@ -0,0 +1,19 @@ +{ + "name": "ProEnFo", + "source": "https://github.com/Leo-VK/EnFoAV", + "desp": "Dataset from the paper Benchmarks and Custom Package for Electrical Load Forecasting. The dataset contains load and external variables like air temperature.", + "domain": "Industry", + "license": "BSD 3-Clause", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "h" + ], + "num_series": 9, + "num_timesteps": 229126, + "num_datapoints": 5312254, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/ProEnFo/references.bib b/ProEnFo/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..3cedf51c1006a9e0389616dd8729f2fe03cfa12a --- /dev/null +++ b/ProEnFo/references.bib @@ -0,0 +1,6 @@ +@article{wang2023benchmarks, + title={Benchmarks and Custom Package for Energy Forecasting}, + author={Wang, Zhixian and Wen, Qingsong and Zhang, Chaoli and Sun, Liang and Von Krannichfeldt, Leandro and Pan, Shirui and Wang, Yi}, + journal={arXiv preprint arXiv:2307.07191}, + year={2023} +} \ No newline at end of file diff --git a/Pydaq/meta.json b/Pydaq/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..29e3f41d31bf00613c6ea57d96779a085351cffd --- /dev/null +++ b/Pydaq/meta.json @@ -0,0 +1,19 @@ +{ + "name": "Pvdaq", + "source": "https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pvdaq%2F", + "desp": "Pydaq dataset.", + "domain": "Energy", + "license": "Apache-2.0", + "targets": [], + "covariates": [], + "timestamp": "measured_on", + "freq": [ + "15min" + ], + "num_series": 15, + "num_timesteps": 3968457, + "num_datapoints": 8209270, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/Rideshare/meta.json b/Rideshare/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..f51405bce7ba1e991c98099b85deaa28e541a77e --- /dev/null +++ b/Rideshare/meta.json @@ -0,0 +1,19 @@ +{ + "name": "Rideshare", + "source": "https://zenodo.org/records/5122232", + "desp": "This dataset contains various hourly time series representations of attributes related to Uber and Lyft rideshare services for various locations in New York between 26/11/2018 and 18/12/2018.", + "domain": "Traffic", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "h" + ], + "num_series": 1, + "num_timesteps": 193, + "num_datapoints": 378859, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/Rideshare/references.bib b/Rideshare/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..851e7e8955485f6ce6a9e09c3da1ab58908b57a0 --- /dev/null +++ b/Rideshare/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2021_5122232, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Rideshare Dataset without Missing Values}, + journal = {Zenodo}, + year = {2021} +} diff --git a/RoomOccupancy/meta.json b/RoomOccupancy/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..a5b7ba34f213acffbf8eee97fad6180b70908cca --- /dev/null +++ b/RoomOccupancy/meta.json @@ -0,0 +1,38 @@ +{ + "name": "RoomOccupancy", + "source": "https://archive.ics.uci.edu/dataset/864/room+occupancy+estimation", + "desp": "Data set for estimating the precise number of occupants in a room using multiple non-intrusive environmental sensors like temperature, light, sound, CO2 and PIR.", + "domain": "Industry", + "license": "CC BY 4.0", + "targets": [ + "Room_Occupancy_Count" + ], + "covariates": [ + "S2_Temp", + "S3_Sound", + "S7_PIR", + "S3_Light", + "S4_Temp", + "S4_Light", + "S2_Sound", + "S6_PIR", + "S1_Temp", + "S1_Sound", + "S4_Sound", + "S5_CO2_Slope", + "S1_Light", + "S3_Temp", + "S2_Light", + "S5_CO2" + ], + "timestamp": null, + "freq": [ + "30s" + ], + "num_series": 1, + "num_timesteps": 10129, + "num_datapoints": 172193, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/RoomOccupancy/references.bib b/RoomOccupancy/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..13b76be272622385dc9cae9d823912a12ded96ea --- /dev/null +++ b/RoomOccupancy/references.bib @@ -0,0 +1,6 @@ +@article{room_occupancy_estimation_864, + author = {Singh, Adarsh Pal and Chaudhari, Sachin}, + title = {Room Occupancy Estimation}, + journal = {UCI Machine Learning Repository}, + year = {2018} +} diff --git a/SHandHZMetro/meta.json b/SHandHZMetro/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..515d062a6fa460f9cdeada807627cb6857444d2d --- /dev/null +++ b/SHandHZMetro/meta.json @@ -0,0 +1,19 @@ +{ + "name": "SHandHZMetro", + "source": "https://github.com/LibCity/Bigscity-LibCity-Datasets", + "desp": "Traffic flows from From metro stations in Shanghai and Hangzhou.", + "domain": "Traffic", + "license": "Apache-2.0", + "targets": [], + "covariates": [], + "timestamp": "0", + "freq": [ + "15min" + ], + "num_series": 32, + "num_timesteps": 84480, + "num_datapoints": 20376576, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/SHandHZMetro/references.bib b/SHandHZMetro/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..308f8a678c41a2ec2e0201fad17fc3787db1ebd7 --- /dev/null +++ b/SHandHZMetro/references.bib @@ -0,0 +1,7 @@ +@inproceedings{10.1145/3474717.3483923, + author = {Wang, Jingyuan and Jiang, Jiawei and Jiang, Wenjun and Li, Chao and Zhao, Wayne Xin}, + title = {LibCity: {A}n Open Library for Traffic Prediction}, + year = {2021}, + booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems}, + pages = {145--148} +} \ No newline at end of file diff --git a/SP500/meta.json b/SP500/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..fd7187349bdf15a21acf0d7e4065a3af83c78f10 --- /dev/null +++ b/SP500/meta.json @@ -0,0 +1,23 @@ +{ + "name": "SP500", + "source": "https://github.com/liorsidi/sp500-stock-similarity-time-series/tree/master", + "desp": "The S&P dataset contains daily historical data for all the S&P (Standard & Poor) 500 stock market index companies from 2012 to 2017. The features given are date, open price, closing price, highest price, lowest price, volume, and the short name of the stock. The S&P is an American stock index of the largest companies listed in NYSE or NASDAQ, maintained by S&P Dow Jones Indices. It covers about 80 percent of the American equity market by capitalization.", + "domain": "Finance", + "license": "Unknown", + "targets": [ + "Low", + "Open", + "High", + "Volume", + "Close" + ], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 500, + "num_timesteps": 603027, + "num_datapoints": 3013877, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/SP500/references.bib b/SP500/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..d894dcdc8d6b00fe81613f79a17233c79472c1ce --- /dev/null +++ b/SP500/references.bib @@ -0,0 +1,6 @@ +@article{sidi2020improving, + title={Improving S\&P stock prediction with time series stock similarity}, + author={Sidi, Lior}, + journal={arXiv preprint arXiv:2002.05784}, + year={2020} +} \ No newline at end of file diff --git a/SP500KnownOpen/meta.json b/SP500KnownOpen/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..03e9a992b2f850e0882e651a49e90bb8c735bfb5 --- /dev/null +++ b/SP500KnownOpen/meta.json @@ -0,0 +1,24 @@ +{ + "name": "SP500KnownOpen", + "source": "https://github.com/liorsidi/sp500-stock-similarity-time-series/tree/master", + "desp": "The S&P dataset contains daily historical data for all the S&P (Standard & Poor) 500 stock market index companies from 2012 to 2017. The features given are date, open price, closing price, highest price, lowest price, volume, and the short name of the stock. The S&P is an American stock index of the largest companies listed in NYSE or NASDAQ, maintained by S&P Dow Jones Indices. It covers about 80 percent of the American equity market by capitalization. Assume open price is known.", + "domain": "Finance", + "license": "Unknown", + "targets": [ + "Low", + "High", + "Volume", + "Close" + ], + "covariates": [ + "Open" + ], + "timestamp": null, + "freq": null, + "num_series": 500, + "num_timesteps": 603027, + "num_datapoints": 3013877, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/SP500KnownOpen/references.bib b/SP500KnownOpen/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..d894dcdc8d6b00fe81613f79a17233c79472c1ce --- /dev/null +++ b/SP500KnownOpen/references.bib @@ -0,0 +1,6 @@ +@article{sidi2020improving, + title={Improving S\&P stock prediction with time series stock similarity}, + author={Sidi, Lior}, + journal={arXiv preprint arXiv:2002.05784}, + year={2020} +} \ No newline at end of file diff --git a/SWAT/meta.json b/SWAT/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..8b7ad7dc7428db312139c9931c3fb4f8a4639dc5 --- /dev/null +++ b/SWAT/meta.json @@ -0,0 +1,72 @@ +{ + "name": "SWAT", + "source": "https://www.kaggle.com/datasets/abdullahk1h2a3n/swatdataset/data", + "desp": "Data set for sensors and actuators readings from a secure water treatment testbed.", + "domain": "Industry", + "license": "Unknown", + "targets": [ + "FIT101", + "LIT101", + "AIT201", + "AIT202", + "AIT203", + "FIT201", + "DPIT301", + "FIT301", + "LIT301", + "AIT401", + "AIT402", + "FIT401", + "LIT401", + "AIT501", + "AIT502", + "AIT503", + "AIT504", + "FIT501", + "FIT502", + "FIT503", + "FIT504", + "PIT501", + "PIT502", + "PIT503", + "FIT601" + ], + "covariates": [ + "MV101", + "P101", + "P102", + "MV201", + "P201", + "P202", + "P203", + "P204", + "P205", + "P206", + "MV301", + "MV302", + "MV303", + "MV304", + "P301", + "P302", + "P401", + "P402", + "P403", + "P404", + "UV401", + "P501", + "P502", + "P601", + "P602", + "P603" + ], + "timestamp": null, + "freq": [ + "5s" + ], + "num_series": 2, + "num_timesteps": 189344, + "num_datapoints": 7933696, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/SWAT/references.bib b/SWAT/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..4032a33c444b3dc9d61cec9d77d245f9057542e4 --- /dev/null +++ b/SWAT/references.bib @@ -0,0 +1,6 @@ +@inproceedings{goh_2017_securewater, + author = {Goh, J. and Adepu, S. and Junejo, K. N. and Mathur, A.}, + title = {A Dataset to Support Research in the Design of Secure Water Treatment Systems}, + booktitle = {Proceedings of the 2016 Critical Information Infrastructures Security}, + year = {2016} +} diff --git a/Satellite/meta.json b/Satellite/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e6066ac71f6147ea2973db7d92b6315788ba0df5 --- /dev/null +++ b/Satellite/meta.json @@ -0,0 +1,17 @@ +{ + "name": "Satellite", + "source": "https://github.com/khundman/telemanom", + "desp": "Real spacecraft telemetry data and anomalies from the Soil Moisture Active Passive satellite (SMAP) and the Curiosity Rover on Mars (MSL). All data has been anonymized with regard to time and all telemetry values are pre-scaled between (-1,1) according to the min/max in the test set. Channel IDs are also anonymized, but the first letter gives indicates the type of channel (P = power, R = radiation, etc.). Model input data also includes one-hot encoded information about commands that were sent or received by specific spacecraft modules in a given time window. No identifying information related to the timing or nature of commands is included in the data.", + "domain": "Others", + "license": "Apache-2.0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 80, + "num_timesteps": 194944, + "num_datapoints": 2906716, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/Satellite/references.bib b/Satellite/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..61127a2d1f3c994ae384f07b716b29b1c6bfe236 --- /dev/null +++ b/Satellite/references.bib @@ -0,0 +1,6 @@ +@article{hundman_2018_spacecraft, + author = {Hundman, Kyle and Constantinou, Valentino and Laporte, Christopher and Colwell, Ian and Soderstrom, Tom}, + title = {Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding}, + journal = {arXiv preprint arXiv:1802.04431}, + year = {2018} +} diff --git a/ServerMachineDataset/meta.json b/ServerMachineDataset/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..6adb5343b645870bf18a3b23a3020f3b41b625d8 --- /dev/null +++ b/ServerMachineDataset/meta.json @@ -0,0 +1,17 @@ +{ + "name": "ServerMachineDataset", + "source": "https://github.com/NetManAIOps/OmniAnomaly", + "desp": "SMD (Server Machine Dataset) is a 5-week-long dataset collected from a large Internet company. It is made up by data from 28 different machines.", + "domain": "Industry", + "license": "MIT", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 28, + "num_timesteps": 708405, + "num_datapoints": 21991847, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/ServerMachineDataset/references.bib b/ServerMachineDataset/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..6d4e1dfd66f1c2c7328d7ce0753e52d2a0a785cf --- /dev/null +++ b/ServerMachineDataset/references.bib @@ -0,0 +1,7 @@ +@inproceedings{su2019robust, + title={Robust Anomaly Detection for Multivariate Time Series Through Stochastic Recurrent Neural Network}, + author={Su, Ya and Zhao, Youjian and Niu, Chenhao and Liu, Rong and Sun, Wei and Pei, Dan}, + booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, + pages={2828--2837}, + year={2019} +} \ No newline at end of file diff --git a/ShellHackathon/meta.json b/ShellHackathon/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..aaeac295eeaf3a03118f2a6e981cdc7f314af424 --- /dev/null +++ b/ShellHackathon/meta.json @@ -0,0 +1,34 @@ +{ + "name": "ShellHackathon", + "source": "https://www.kaggle.com/datasets/dilipkola/shell-ai-solar-irradiance-prediction-hackathon", + "desp": "Solar power is one of the fastest growing renewable energy sources. However, there is a major challenge associated with solar power production, and that is its intermittency caused by variable weather conditions. Of course, we know the sun is not going to shine at night. Predicting solar power output during the day is key for grid operators to balance energy generation and consumption.", + "domain": "Energy", + "license": "Unknown", + "targets": [ + "Global CMP22 (vent/cor) [W/m^2]" + ], + "covariates": [ + "Moisture", + "Azimuth Angle [degrees]", + "Station Pressure [mBar]", + "Snow Depth [cm]", + "Total Cloud Cover [%]", + "Tower Wet Bulb Temp [deg C]", + "Precipitation (Accumulated) [mm]", + "Direct sNIP [W/m^2]", + "Tower RH [%]", + "Tower Dry Bulb Temp [deg C]", + "Tower Dew Point Temp [deg C]", + "Albedo (CMP11)", + "Avg Wind Direction @ 6ft [deg from N]", + "Peak Wind Speed @ 6ft [m/s]" + ], + "timestamp": null, + "freq": null, + "num_series": 1, + "num_timesteps": 527040, + "num_datapoints": 7905600, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/Solar10Minutes/meta.json b/Solar10Minutes/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..6d50d626c0f54ef357bbd69ef2243711f0fc8228 --- /dev/null +++ b/Solar10Minutes/meta.json @@ -0,0 +1,19 @@ +{ + "name": "Solar10Minutes", + "source": "https://zenodo.org/records/4656144", + "desp": "The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006.", + "domain": "Energy", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "10min" + ], + "num_series": 1, + "num_timesteps": 52560, + "num_datapoints": 7200720, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/Solar10Minutes/references.bib b/Solar10Minutes/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..8dc9053013f7bb4889a10bcde51e2dd09bad82c0 --- /dev/null +++ b/Solar10Minutes/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2020_solar10min, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Solar Dataset (10 Minutes Observations)}, + journal = {Zenodo}, + year = {2020} +} diff --git a/Solar4Seconds/meta.json b/Solar4Seconds/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..c50b3fcac4aa44ef1e96c4a10efbd745e0870d9d --- /dev/null +++ b/Solar4Seconds/meta.json @@ -0,0 +1,21 @@ +{ + "name": "Solar4Seconds", + "source": "https://zenodo.org/records/4656027", + "desp": "This dataset contains a single very long daily time series representing the solar power production in MW recorded per every 4 seconds starting from 01/08/2019. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. The length of this time series is 7397222.", + "domain": "Energy", + "license": "CC BY 4.0", + "targets": [ + "T1" + ], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "4s" + ], + "num_series": 1, + "num_timesteps": 7397222, + "num_datapoints": 7397222, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/Solar4Seconds/references.bib b/Solar4Seconds/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..1a23df0fd2382c246ece519512a22ec794dd2693 --- /dev/null +++ b/Solar4Seconds/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2020_solar, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Abolghasemi, Mahdi and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Solar Power Dataset (4 Seconds Observations)}, + journal = {Zenodo}, + year = {2020} +} diff --git a/SolarEnergy/meta.json b/SolarEnergy/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..62defed15bd243ea452b09152b7bc681fe25464d --- /dev/null +++ b/SolarEnergy/meta.json @@ -0,0 +1,157 @@ +{ + "name": "SolarEnergy", + "source": "https://github.com/laiguokun/multivariate-time-series-data", + "desp": "It contains the solar power production records in the year of 2006, which is sampled every 10 minutes from 137 PV plants in Alabama State.", + "domain": "Energy", + "license": "Unknown", + "targets": [ + "f112", + "f23", + "f64", + "f89", + "f5", + "f67", + "f17", + "f117", + "f15", + "f46", + "f25", + "f36", + "f59", + "f130", + "f1", + "f6", + "f20", + "f55", + "f105", + "f24", + "f106", + "f70", + "f79", + "f13", + "f114", + "f39", + "f74", + "f75", + "f68", + "f72", + "f102", + "f115", + "f16", + "f18", + "f60", + "f110", + "f11", + "f121", + "f91", + "f43", + "f33", + "f87", + "f28", + "f58", + "f9", + "f76", + "f133", + "f104", + "f26", + "f126", + "f100", + "f31", + "f45", + "f37", + "f52", + "f88", + "f4", + "f41", + "f14", + "f47", + "f21", + "f137", + "f84", + "f82", + "f19", + "f54", + "f80", + "f27", + "f65", + "f136", + "f129", + "f78", + "f95", + "f71", + "f50", + "f127", + "f90", + "f2", + "f113", + "f116", + "f92", + "f69", + "f56", + "f10", + "f86", + "f73", + "f44", + "f66", + "f93", + "f120", + "f32", + "f123", + "f85", + "f108", + "f98", + "f34", + "f49", + "f29", + "f42", + "f107", + "f83", + "f118", + "f134", + "f128", + "f51", + "f77", + "f81", + "f94", + "f125", + "f97", + "f131", + "f57", + "f99", + "f53", + "f22", + "f3", + "f12", + "f103", + "f119", + "f135", + "f124", + "f63", + "f40", + "f101", + "f8", + "f122", + "f48", + "f61", + "f7", + "f132", + "f96", + "f38", + "f109", + "f111", + "f35", + "f30", + "f62" + ], + "covariates": [], + "timestamp": null, + "freq": [ + "10min" + ], + "num_series": 1, + "num_timesteps": 52560, + "num_datapoints": 7200720, + "split": "test", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/SolarEnergy/references.bib b/SolarEnergy/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..c5e8a51670de013a677960cc51f9ac7218f8ecf3 --- /dev/null +++ b/SolarEnergy/references.bib @@ -0,0 +1,7 @@ +@inproceedings{lai2018modeling, + title={Modeling Long-and Short-Term Temporal Patterns with Deep Neural Networks}, + author={Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao}, + booktitle={Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, + pages={95--104}, + year={2018} +} \ No newline at end of file diff --git a/StockMarketData/meta.json b/StockMarketData/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..1888a0d251db55e3f16f4a362cfba11e7993cf10 --- /dev/null +++ b/StockMarketData/meta.json @@ -0,0 +1,17 @@ +{ + "name": "StockMarketData", + "source": "https://www.kaggle.com/datasets/paultimothymooney/stock-market-data", + "desp": "Date, Volume, High, Low, and Closing Price (for all NASDAQ, S&P500, and NYSE listed companies).", + "domain": "Finance", + "license": "Unknown", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 5, + "num_timesteps": 9920, + "num_datapoints": 694400, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/Sunspots/meta.json b/Sunspots/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..f71fb2857b25e96d7741355e557ae8310153f472 --- /dev/null +++ b/Sunspots/meta.json @@ -0,0 +1,19 @@ +{ + "name": "Sunspots", + "source": "https://www.kaggle.com/datasets/robervalt/sunspots?ref=hackernoon.com", + "desp": "Sunspots are temporary phenomena on the Sun's photosphere that appear as spots darker than the surrounding areas. They are regions of reduced surface temperature caused by concentrations of magnetic field flux that inhibit convection. Sunspots usually appear in pairs of opposite magnetic polarity. Their number varies according to the approximately 11-year solar cycle.", + "domain": "Others", + "license": "CC0", + "targets": [ + "Monthly Mean Total Sunspot Number" + ], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 1, + "num_timesteps": 3265, + "num_datapoints": 3265, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/TemperatureRain/meta.json b/TemperatureRain/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..95894c536fbea8cac66f8bc7ee99517c48b53974 --- /dev/null +++ b/TemperatureRain/meta.json @@ -0,0 +1,19 @@ +{ + "name": "TemperatureRain", + "source": "https://zenodo.org/records/5129091", + "desp": "This dataset contains 32072 daily time series showing the temperature observations and rain forecasts, gathered by the Australian Bureau of Meteorology for 422 weather stations across Australia, between 02/05/2015 and 26/04/2017.", + "domain": "Environment", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "d" + ], + "num_series": 1, + "num_timesteps": 725, + "num_datapoints": 1165075, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/TemperatureRain/references.bib b/TemperatureRain/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..49a09976894661ccd9b8dcac1c05f08af1083306 --- /dev/null +++ b/TemperatureRain/references.bib @@ -0,0 +1,6 @@ +@article{godahewa_2021_temperature_rain, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Temperature Rain Dataset without Missing Values}, + journal = {Zenodo}, + year = {2021} +} diff --git a/TetuanPowerConsumption/meta.json b/TetuanPowerConsumption/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..8dc1fbb30e1e348773719dfc04db86609a9b93c7 --- /dev/null +++ b/TetuanPowerConsumption/meta.json @@ -0,0 +1,29 @@ +{ + "name": "TetuanPowerConsumption", + "source": "https://archive.ics.uci.edu/dataset/849/power+consumption+of+tetouan+city", + "desp": "This dataset is related to power consumption of three different distribution networks of Tetouan city which is located in north Morocco", + "domain": "Energy", + "license": "CC BY 4.0", + "targets": [ + "Zone 1 Power Consumption", + "Zone 3 Power Consumption", + "Zone 2 Power Consumption" + ], + "covariates": [ + "Humidity", + "Wind Speed", + "diffuse flows", + "general diffuse flows", + "Temperature" + ], + "timestamp": "DateTime", + "freq": [ + "10min" + ], + "num_series": 1, + "num_timesteps": 52416, + "num_datapoints": 419328, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/TetuanPowerConsumption/references.bib b/TetuanPowerConsumption/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..8b3ca835ce9bf168fd0991bdf09a9b86b3c56324 --- /dev/null +++ b/TetuanPowerConsumption/references.bib @@ -0,0 +1,6 @@ +@article{salam_2018_tetouan_power, + author = {Salam, Abdulwahed and El Hibaoui, Abdelaaziz}, + title = {Power Consumption of Tetouan City}, + journal = {UCI Machine Learning Repository}, + year = {2018} +} diff --git a/Tigge/meta.json b/Tigge/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..409ae1be12ad02cfe829daf16b9a8f070340c8e5 --- /dev/null +++ b/Tigge/meta.json @@ -0,0 +1,19 @@ +{ + "name": "Tigge", + "source": "https://github.com/pangeo-data/WeatherBench", + "desp": "Tigge dataset from Weather Bench.", + "domain": "Environment", + "license": "MIT", + "targets": [], + "covariates": [], + "timestamp": "time", + "freq": [ + "6h" + ], + "num_series": 768, + "num_timesteps": 108032, + "num_datapoints": 21008944, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/Tigge/references.bib b/Tigge/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..cbe5835f5bfc0cdb9e53e82d713b914251e8261e --- /dev/null +++ b/Tigge/references.bib @@ -0,0 +1,8 @@ +@article{Rasp_2020, + title={WeatherBench: {A} Benchmark Data Set for Data‐Driven Weather Forecasting}, + volume={12}, + number={11}, + journal={Journal of Advances in Modeling Earth Systems}, + author={Rasp, Stephan and Dueben, Peter D. and Scher, Sebastian and Weyn, Jonathan A. and Mouatadid, Soukayna and Thuerey, Nils}, + year={2020} +} diff --git a/TimeMMD/meta.json b/TimeMMD/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..3c8e76790ef94fb64aa45a0e57ad87ac5218224f --- /dev/null +++ b/TimeMMD/meta.json @@ -0,0 +1,21 @@ +{ + "name": "TimeMMD", + "source": "https://github.com/AdityaLab/Time-MMD", + "desp": "Time-MMD is the first multi-domain, multimodal time series dataset covering 9 primary data domains.", + "domain": "Others", + "license": "Unknown", + "targets": [], + "covariates": [], + "timestamp": "date", + "freq": [ + "w", + "d", + "ms" + ], + "num_series": 10, + "num_timesteps": 24586, + "num_datapoints": 70518, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/TimeMMD/references.bib b/TimeMMD/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..6f6d8b67fb8da3945c9e401ccf04d028ee8246fc --- /dev/null +++ b/TimeMMD/references.bib @@ -0,0 +1,6 @@ +@article{liu2024timemmd, + title = {Time-MMD: {A} New Multi-Domain Multimodal Dataset for Time Series Analysis}, + author = {Liu, Haoxin and Xu, Shangqing and Zhao, Zhiyuan and Kong, Lingkai and Kamarthi, Harshavardhan and Sasanur, Aditya B. and Sharma, Megha and Cui, Jiaming and Wen, Qingsong and Zhang, Chao and Prakash, B. Aditya}, + journal = {arXiv preprint arXiv:2406.08627}, + year = {2024} +} diff --git a/Traffic/meta.json b/Traffic/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..b7d5f7104ee498b32b4e5c6567a3da14d01c3c09 --- /dev/null +++ b/Traffic/meta.json @@ -0,0 +1,882 @@ +{ + "name": "Traffic", + "source": "https://github.com/laiguokun/multivariate-time-series-data", + "desp": "The data in this repo is a collection of 48 months (2015-2016) hourly data from the California Department of Transportation.The data describes the road occupancy rates (between 0 and 1) measured by different sensors on San Francisco Bay area freeways.", + "domain": "Others", + "license": "Unknown", + "targets": [ + "f494", + "f225", + "f105", + "f512", + "f278", + "f604", + "f516", + "f638", + "f294", + "f651", + "f330", + "f265", + "f240", + "f283", + "f114", + "f685", + "f818", + "f489", + "f11", + "f219", + "f38", + "f261", + "f249", + "f251", + "f40", + "f478", + "f850", + "f462", + "f213", + "f383", + "f285", + "f537", + "f614", + "f695", + "f46", + "f273", + "f195", + "f608", + "f793", + "f487", + "f753", + "f835", + "f63", + "f185", + "f631", + "f673", + "f809", + "f26", + "f760", + "f104", + "f29", + "f221", + "f510", + "f295", + "f352", + "f570", + "f600", + "f186", + "f192", + "f571", + "f54", + "f147", + "f222", + "f238", + "f156", + "f663", + "f519", + "f248", + "f271", + "f596", + "f310", + "f680", + "f798", + "f688", + "f528", + "f672", + "f143", + "f543", + "f825", + "f541", + "f670", + "f27", + "f217", + "f155", + "f133", + "f306", + "f216", + "f257", + "f593", + "f682", + "f721", + "f90", + "f475", + "f854", + "f42", + "f761", + "f513", + "f311", + "f281", + "f164", + "f79", + "f612", + "f795", + "f208", + "f12", + "f465", + "f637", + "f426", + "f595", + "f554", + "f472", + "f410", + "f89", + "f92", + "f126", + "f375", + "f748", + "f546", + "f136", + "f664", + "f434", + "f705", + "f711", + "f284", + "f160", + "f244", + "f202", + "f359", + "f674", + "f520", + "f16", + "f74", + "f234", + "f757", + "f780", + "f413", + "f134", + "f110", + "f169", + "f140", + "f23", + "f122", + "f742", + "f361", + "f493", + "f665", + "f431", + "f255", + "f177", + "f643", + "f860", + "f439", + "f350", + "f429", + "f561", + "f642", + "f125", + "f580", + "f66", + "f381", + "f39", + "f247", + "f380", + "f790", + "f436", + "f280", + "f794", + "f131", + "f339", + "f256", + "f437", + "f10", + "f490", + "f61", + "f165", + "f589", + "f582", + "f635", + "f563", + "f538", + "f686", + "f347", + "f184", + "f319", + "f632", + "f556", + "f702", + "f157", + "f796", + "f374", + "f767", + "f723", + "f3", + "f123", + "f585", + "f514", + "f701", + "f598", + "f617", + "f844", + "f9", + "f124", + "f291", + "f698", + "f52", + "f112", + "f305", + "f759", + "f297", + "f807", + "f811", + "f655", + "f451", + "f848", + "f805", + "f855", + "f808", + "f296", + "f187", + "f91", + "f845", + "f804", + "f603", + "f312", + "f227", + "f50", + "f168", + "f318", + "f620", + "f799", + "f455", + "f524", + "f575", + "f96", + "f530", + "f333", + "f392", + "f749", + "f523", + "f817", + "f751", + "f676", + "f736", + "f836", + "f8", + "f73", + "f152", + "f506", + "f791", + "f419", + "f158", + "f193", + "f34", + "f385", + "f72", + "f171", + "f267", + "f763", + "f533", + "f653", + "f167", + "f828", + "f747", + "f17", + "f587", + "f62", + "f704", + "f734", + "f724", + "f779", + "f752", + "f574", + "f403", + "f652", + "f162", + "f700", + "f151", + "f846", + "f144", + "f259", + "f14", + "f78", + "f649", + "f243", + "f762", + "f364", + "f548", + "f423", + "f650", + "f829", + "f717", + "f449", + "f409", + "f457", + "f562", + "f827", + "f858", + "f301", + "f440", + "f438", + "f412", + "f483", + "f76", + "f540", + "f19", + "f755", + "f460", + "f200", + "f405", + "f719", + "f99", + "f207", + "f547", + "f106", + "f287", + "f756", + "f445", + "f458", + "f683", + "f601", + "f722", + "f729", + "f679", + "f639", + "f180", + "f214", + "f324", + "f660", + "f390", + "f503", + "f327", + "f190", + "f307", + "f446", + "f694", + "f229", + "f583", + "f693", + "f842", + "f647", + "f477", + "f313", + "f212", + "f803", + "f800", + "f132", + "f424", + "f448", + "f394", + "f41", + "f690", + "f658", + "f710", + "f470", + "f108", + "f820", + "f269", + "f559", + "f834", + "f43", + "f277", + "f407", + "f138", + "f332", + "f348", + "f568", + "f400", + "f86", + "f367", + "f428", + "f610", + "f253", + "f321", + "f550", + "f629", + "f95", + "f557", + "f430", + "f362", + "f174", + "f560", + "f44", + "f25", + "f463", + "f681", + "f607", + "f822", + "f299", + "f726", + "f777", + "f532", + "f28", + "f468", + "f196", + "f744", + "f830", + "f300", + "f718", + "f153", + "f678", + "f616", + "f139", + "f814", + "f368", + "f389", + "f500", + "f813", + "f360", + "f346", + "f826", + "f646", + "f735", + "f189", + "f303", + "f671", + "f416", + "f93", + "f237", + "f18", + "f101", + "f521", + "f551", + "f282", + "f369", + "f797", + "f173", + "f197", + "f245", + "f358", + "f525", + "f386", + "f715", + "f115", + "f119", + "f205", + "f495", + "f605", + "f778", + "f801", + "f738", + "f396", + "f366", + "f45", + "f67", + "f88", + "f224", + "f422", + "f466", + "f395", + "f341", + "f331", + "f344", + "f553", + "f58", + "f542", + "f127", + "f425", + "f404", + "f211", + "f363", + "f335", + "f504", + "f49", + "f474", + "f498", + "f302", + "f590", + "f824", + "f33", + "f85", + "f338", + "f565", + "f427", + "f501", + "f745", + "f837", + "f743", + "f569", + "f630", + "f107", + "f697", + "f831", + "f329", + "f496", + "f81", + "f555", + "f420", + "f51", + "f379", + "f584", + "f353", + "f130", + "f677", + "f241", + "f435", + "f781", + "f746", + "f581", + "f545", + "f628", + "f447", + "f769", + "f728", + "f611", + "f508", + "f378", + "f298", + "f469", + "f142", + "f421", + "f266", + "f522", + "f739", + "f732", + "f1", + "f459", + "f518", + "f176", + "f71", + "f544", + "f613", + "f65", + "f356", + "f337", + "f741", + "f654", + "f37", + "f236", + "f776", + "f103", + "f275", + "f377", + "f644", + "f669", + "f488", + "f98", + "f87", + "f215", + "f579", + "f170", + "f179", + "f758", + "f401", + "f476", + "f453", + "f393", + "f372", + "f661", + "f467", + "f159", + "f619", + "f775", + "f511", + "f535", + "f231", + "f308", + "f634", + "f840", + "f786", + "f35", + "f24", + "f792", + "f304", + "f785", + "f397", + "f246", + "f703", + "f235", + "f594", + "f832", + "f94", + "f274", + "f376", + "f21", + "f657", + "f166", + "f787", + "f355", + "f442", + "f260", + "f70", + "f567", + "f32", + "f656", + "f233", + "f326", + "f774", + "f22", + "f315", + "f161", + "f812", + "f839", + "f851", + "f181", + "f857", + "f492", + "f182", + "f708", + "f509", + "f770", + "f226", + "f402", + "f83", + "f684", + "f77", + "f667", + "f293", + "f4", + "f218", + "f194", + "f853", + "f209", + "f178", + "f118", + "f577", + "f766", + "f578", + "f145", + "f515", + "f754", + "f20", + "f627", + "f696", + "f709", + "f354", + "f191", + "f111", + "f31", + "f529", + "f336", + "f175", + "f406", + "f198", + "f230", + "f292", + "f382", + "f2", + "f288", + "f626", + "f552", + "f290", + "f371", + "f727", + "f591", + "f636", + "f714", + "f263", + "f117", + "f443", + "f57", + "f461", + "f633", + "f841", + "f861", + "f486", + "f618", + "f692", + "f768", + "f314", + "f573", + "f497", + "f80", + "f572", + "f789", + "f481", + "f479", + "f482", + "f843", + "f13", + "f228", + "f539", + "f116", + "f415", + "f666", + "f258", + "f706", + "f432", + "f84", + "f480", + "f272", + "f254", + "f316", + "f772", + "f784", + "f121", + "f399", + "f675", + "f852", + "f206", + "f414", + "f183", + "f819", + "f264", + "f343", + "f349", + "f351", + "f357", + "f531", + "f659", + "f856", + "f609", + "f47", + "f48", + "f56", + "f68", + "f154", + "f146", + "f473", + "f641", + "f645", + "f816", + "f309", + "f499", + "f450", + "f150", + "f268", + "f370", + "f55", + "f731", + "f838", + "f640", + "f201", + "f737", + "f833", + "f276", + "f342", + "f859", + "f149", + "f97", + "f454", + "f536", + "f252", + "f549", + "f648", + "f733", + "f750", + "f129", + "f334", + "f622", + "f765", + "f418", + "f668", + "f232", + "f484", + "f411", + "f120", + "f802", + "f823", + "f59", + "f662", + "f588", + "f53", + "f109", + "f322", + "f625", + "f471", + "f602", + "f373", + "f564", + "f815", + "f102", + "f270", + "f325", + "f220", + "f810", + "f689", + "f345", + "f740", + "f558", + "f388", + "f788", + "f137", + "f599", + "f699", + "f250", + "f279", + "f323", + "f491", + "f239", + "f716", + "f485", + "f384", + "f30", + "f534", + "f782", + "f408", + "f527", + "f148", + "f172", + "f204", + "f502", + "f849", + "f526", + "f441", + "f623", + "f720", + "f398", + "f417", + "f82", + "f621", + "f707", + "f576", + "f188", + "f69", + "f64", + "f203", + "f223", + "f586", + "f764", + "f433", + "f387", + "f691", + "f464", + "f141", + "f773", + "f592", + "f712", + "f730", + "f75", + "f100", + "f456", + "f821", + "f36", + "f242", + "f340", + "f771", + "f289", + "f286", + "f687", + "f128", + "f365", + "f60", + "f606", + "f444", + "f199", + "f862", + "f615", + "f452", + "f505", + "f624", + "f847", + "f135", + "f113", + "f713", + "f320", + "f317", + "f7", + "f262", + "f725", + "f6", + "f517", + "f806", + "f783", + "f210", + "f391", + "f566", + "f328", + "f507", + "f5", + "f597", + "f15", + "f163" + ], + "covariates": [], + "timestamp": null, + "freq": [ + "h" + ], + "num_series": 1, + "num_timesteps": 17544, + "num_datapoints": 15122928, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/Traffic/references.bib b/Traffic/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..614e271cbdb9ea146d9bdc3119bb88d2e4d87b12 --- /dev/null +++ b/Traffic/references.bib @@ -0,0 +1,7 @@ +@inproceedings{lai2018modeling, + title={Modeling Long-and Short-Term Temporal Patterns with Deep Neural Networks}, + author={Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao}, + booktitle={Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, + pages={95--104}, + year={2018} +} \ No newline at end of file diff --git a/TrafficHourly/meta.json b/TrafficHourly/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..960241a50839625f41b38c4c502de115436fbdbd --- /dev/null +++ b/TrafficHourly/meta.json @@ -0,0 +1,19 @@ +{ + "name": "TrafficHourly", + "source": "https://zenodo.org/records/4656132", + "desp": "This dataset contains the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 hourly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.", + "domain": "Traffic", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "h" + ], + "num_series": 1, + "num_timesteps": 17544, + "num_datapoints": 15122928, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/TrafficHourly/references.bib b/TrafficHourly/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..0884c30c0b6c87077bf9024b802a8c7fc27fafce --- /dev/null +++ b/TrafficHourly/references.bib @@ -0,0 +1,6 @@ +@article{godahewa2020_traffic_hourly, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + title = {Traffic Hourly Dataset}, + journal = {Zenodo}, + year = {2020} +} diff --git a/UK-DALE/meta.json b/UK-DALE/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e8488210cd4282341f1b060a3702434ba650de5a --- /dev/null +++ b/UK-DALE/meta.json @@ -0,0 +1,46 @@ +{ + "name": "UK-DALE", + "source": "https://jack-kelly.com/data/", + "desp": "UK Domestic Appliance-Level Electricity (UK-DALE) dataset. This dataset records the power demand from five houses. In each house we record both the whole-house mains power demand every six seconds as well as power demand from individual appliances every six seconds.", + "domain": "Energy", + "license": "CC BY 4.0", + "targets": [ + "main_3", + "channel_9", + "channel_39", + "channel_4", + "channel_41", + "main_2", + "channel_13", + "channel_5", + "channel_40", + "channel_36", + "channel_14", + "channel_12", + "channel_1", + "channel_22", + "channel_3", + "main_1" + ], + "covariates": [ + "channel_3_button_press", + "channel_22_button_press", + "channel_14_button_press", + "channel_36_button_press", + "channel_39_button_press", + "channel_12_button_press", + "channel_13_button_press", + "channel_4_button_press", + "channel_9_button_press", + "channel_41_button_press", + "channel_40_button_press" + ], + "timestamp": null, + "freq": null, + "num_series": 27, + "num_timesteps": 30201680, + "num_datapoints": 65602297, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/UK-DALE/references.bib b/UK-DALE/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..8d27cb657bc062caa76907000d95c611bb01d7b9 --- /dev/null +++ b/UK-DALE/references.bib @@ -0,0 +1,8 @@ +@article{kelly2015ukdale, + title = {The UK-DALE dataset: {D}omestic appliance-level electricity demand and whole-house demand from five UK homes}, + author = {Kelly, Jack and Knottenbelt, William}, + journal = {Scientific Data}, + volume = {2}, + number = {150007}, + year = {2015} +} diff --git a/UKEconomy/meta.json b/UKEconomy/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e647e81c9e23cc9c001526e243b4125d32cc89a2 --- /dev/null +++ b/UKEconomy/meta.json @@ -0,0 +1,17 @@ +{ + "name": "UKEconomy", + "source": "https://data.world/ian/3-centuries-of-uk-economy-data", + "desp": "Three centuries of macroeconomic data from Bank of England.", + "domain": "Finance", + "license": "Unknown", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 1248, + "num_timesteps": 392712, + "num_datapoints": 399900, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/USAirPollution/meta.json b/USAirPollution/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..b29134f41d79f3e8bd56b40b6b8a1a472208e2cf --- /dev/null +++ b/USAirPollution/meta.json @@ -0,0 +1,17 @@ +{ + "name": "USAirPollution", + "source": "https://data.world/data-society/us-air-pollution-data", + "desp": "Air Pollution in the U.S. since 2000-2016.", + "domain": "Environment", + "license": "CC0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 165, + "num_timesteps": 1746661, + "num_datapoints": 24450462, + "split": "train", + "quality": "medium", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/USBirths/meta.json b/USBirths/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..27ca503cbc5b0bcc6d02a5a0a358732539bbaf69 --- /dev/null +++ b/USBirths/meta.json @@ -0,0 +1,19 @@ +{ + "name": "USBirths", + "source": "https://zenodo.org/records/4656049", + "desp": "This dataset contains a single very long daily time series representing the number of births in US from 01/01/1969 to 31/12/1988. It was extracted from R mosaicData package. The length of this time series is 7305.", + "domain": "Others", + "license": "CC BY 4.0", + "targets": [ + "T1" + ], + "covariates": [], + "timestamp": null, + "freq": null, + "num_series": 1, + "num_timesteps": 7305, + "num_datapoints": 7305, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/USBirths/references.bib b/USBirths/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..27e2ea765850b19188b5467cb9f449ea42dcab93 --- /dev/null +++ b/USBirths/references.bib @@ -0,0 +1,6 @@ +@article{godahewa2020usbirths, + title = {US Births Dataset}, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + journal = {Zenodo}, + year = {2020} +} diff --git a/VehicleTrips/meta.json b/VehicleTrips/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..c0afc9879f9e4f862d76fc6f5e1fb23b1ef37141 --- /dev/null +++ b/VehicleTrips/meta.json @@ -0,0 +1,19 @@ +{ + "name": "VehicleTrips", + "source": "https://zenodo.org/records/5122535", + "desp": "This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies.", + "domain": "Others", + "license": "CC BY 4.0", + "targets": [], + "covariates": [], + "timestamp": "datetime", + "freq": [ + "d" + ], + "num_series": 1, + "num_timesteps": 212, + "num_datapoints": 848, + "split": "train", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/VehicleTrips/references.bib b/VehicleTrips/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..edcb1e8ccd023fade9fff7cefc5ec1b6fb080e80 --- /dev/null +++ b/VehicleTrips/references.bib @@ -0,0 +1,6 @@ +@article{godahewa2021vehicletrips, + title = {Vehicle Trips Dataset with Missing Values}, + author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, + journal = {Zenodo}, + year = {2021} +} diff --git a/WADI/meta.json b/WADI/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..7d62e5bac0bd094bc867e20eefdff785e6bfbc54 --- /dev/null +++ b/WADI/meta.json @@ -0,0 +1,114 @@ +{ + "name": "WADI", + "source": "https://www.kaggle.com/datasets/giovannimonco/wadi-data", + "desp": "Data set for sensors and actuators readings from a water distribution testbed.", + "domain": "Industry", + "license": "Apache-2.0", + "targets": [ + "1_AIT_001_PV", + "1_AIT_002_PV", + "1_AIT_003_PV", + "1_AIT_004_PV", + "1_AIT_005_PV", + "1_FIT_001_PV", + "1_LT_001_PV", + "2_DPIT_001_PV", + "2_FIC_101_CO", + "2_FIC_101_PV", + "2_FIC_101_SP", + "2_FIC_201_CO", + "2_FIC_201_PV", + "2_FIC_201_SP", + "2_FIC_301_CO", + "2_FIC_301_PV", + "2_FIC_301_SP", + "2_FIC_401_CO", + "2_FIC_401_PV", + "2_FIC_401_SP", + "2_FIC_501_CO", + "2_FIC_501_PV", + "2_FIC_501_SP", + "2_FIC_601_CO", + "2_FIC_601_PV", + "2_FIC_601_SP", + "2_FIT_001_PV", + "2_FIT_002_PV", + "2_FIT_003_PV", + "2_FQ_101_PV", + "2_FQ_201_PV", + "2_FQ_301_PV", + "2_FQ_401_PV", + "2_FQ_501_PV", + "2_FQ_601_PV", + "2_LT_001_PV", + "2_LT_002_PV", + "2_MCV_101_CO", + "2_MCV_201_CO", + "2_MCV_301_CO", + "2_MCV_401_CO", + "2_MCV_501_CO", + "2_MCV_601_CO", + "2_P_003_SPEED", + "2_P_004_SPEED", + "2_PIC_003_CO", + "2_PIC_003_PV", + "2_PIT_001_PV", + "2_PIT_002_PV", + "2_PIT_003_PV", + "2A_AIT_001_PV", + "2A_AIT_002_PV", + "2A_AIT_003_PV", + "2A_AIT_004_PV", + "2B_AIT_001_PV", + "2B_AIT_002_PV", + "2B_AIT_003_PV", + "2B_AIT_004_PV", + "3_AIT_001_PV", + "3_AIT_002_PV", + "3_AIT_003_PV", + "3_AIT_004_PV", + "3_AIT_005_PV", + "3_FIT_001_PV", + "3_LT_001_PV", + "LEAK_DIFF_PRESSURE", + "TOTAL_CONS_REQUIRED_FLOW" + ], + "covariates": [ + "1_MV_001_STATUS", + "1_MV_004_STATUS", + "1_P_001_STATUS", + "1_P_003_STATUS", + "1_P_005_STATUS", + "2_LS_101_AH", + "2_LS_101_AL", + "2_LS_201_AH", + "2_LS_201_AL", + "2_LS_301_AH", + "2_LS_301_AL", + "2_LS_401_AH", + "2_LS_401_AL", + "2_LS_501_AH", + "2_LS_501_AL", + "2_LS_601_AH", + "2_LS_601_AL", + "2_MV_003_STATUS", + "2_MV_006_STATUS", + "2_MV_101_STATUS", + "2_MV_201_STATUS", + "2_MV_301_STATUS", + "2_MV_401_STATUS", + "2_MV_501_STATUS", + "2_MV_601_STATUS", + "2_P_003_STATUS" + ], + "timestamp": null, + "freq": [ + "5s" + ], + "num_series": 2, + "num_timesteps": 257622, + "num_datapoints": 23958846, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/WeatherTest/meta.json b/WeatherTest/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..3082fb784243632cae98835e843257aa67c86698 --- /dev/null +++ b/WeatherTest/meta.json @@ -0,0 +1,39 @@ +{ + "name": "WeatherTest", + "source": "https://www.bgc-jena.mpg.de/wetter/", + "desp": "This dataset is recorded every 10 minutes for 2020 whole year, which contains 21 meteorological indicators, such as air temperature, humidity, etc.", + "domain": "Environment", + "license": "Unknown", + "targets": [ + "Vpmax", + "p", + "max. wv", + "Vpact", + "Tlog", + "wd", + "Tdew", + "sh", + "T", + "Tpot", + "Vpdef", + "raining", + "wv", + "OT", + "max. PAR", + "SWDR", + "rh", + "rho", + "PAR", + "rain", + "H2OC" + ], + "covariates": [], + "timestamp": "date", + "freq": null, + "num_series": 1, + "num_timesteps": 52696, + "num_datapoints": 1106616, + "split": "test", + "quality": "high", + "timestamp_as_covariate": true +} \ No newline at end of file diff --git a/WeeklyFuelPricesItaly/meta.json b/WeeklyFuelPricesItaly/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..93e22002866e65abe208641eb53f5f813742723d --- /dev/null +++ b/WeeklyFuelPricesItaly/meta.json @@ -0,0 +1,19 @@ +{ + "name": "WeeklyFuelPricesItaly", + "source": "https://data.world/rafabelokurows/weekly-fuel-prices-in-italy", + "desp": "Weekly fuel prices in Italy from March 2005 to April 2024.", + "domain": "Finance", + "license": "IODL 2.0", + "targets": [], + "covariates": [], + "timestamp": null, + "freq": [ + "w" + ], + "num_series": 6, + "num_timesteps": 4980, + "num_datapoints": 16600, + "split": "train", + "quality": "very high", + "timestamp_as_covariate": false +} \ No newline at end of file diff --git a/WeeklyRoadFuelPrices/meta.json b/WeeklyRoadFuelPrices/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..6ccc0d3d2c9df7db2a2b0c9d56912c1ad354d1a9 --- /dev/null +++ b/WeeklyRoadFuelPrices/meta.json @@ -0,0 +1,22 @@ +{ + "name": "WeeklyRoadFuelPrices", + "source": "https://data.world/makeovermonday/2020w17-weekly-road-fuel-prices", + "desp": "Weekly road fuel prices.", + "domain": "Finance", + "license": "Unknown", + "targets": [ + "Petrol (USD)", + "Diesel (USD)" + ], + "covariates": [], + "timestamp": null, + "freq": [ + "w" + ], + "num_series": 1, + "num_timesteps": 881, + "num_datapoints": 1762, + "split": "train", + "quality": "high", + "timestamp_as_covariate": false +} \ No newline at end of file