Hugging Face's logo

Datasets:
smilegeng
/
fev_datasets

Tasks:
Time Series Forecasting
Modalities:
Time-series
ArXiv:
Tags:
timeseries
time-series
tsfile
forecasting
License:
Dataset card Files
xet
 Community
smilegeng commited on
Commit
03d2c96
·
verified ·
1 Parent(s): 82154eb

Restore dataset card and add timeseries modality tag

Browse files
Files changed (1) hide show
  1. README.md +104 -104
README.md CHANGED
@@ -1,104 +1,104 @@
1
- ---
2
- license: other
3
- task_categories:
4
- - time-series-forecasting
5
- tags:
6
- - time-series
7
- - tsfile
8
- - forecasting
9
- - timeseries
10
- pretty_name: FEV datasets (TsFile format)
11
- ---
12
-
13
- # FEV 预测数据集合集 — TsFile 格式
14
-
15
- 本仓库是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本，共 **49 个子集**。每个子集一个目录，含 `.tsfile` 数据文件（大表自动分片为多个 `.tsfile`）与说明 `README.md`。
16
-
17
- > 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
18
-
19
- ## 转换说明
20
-
21
- - `id`（每条序列）→ TsFile **device**（TAG 维度）。
22
- - 静态协变量列 → 也作 **TAG**（device 元数据）。
23
- - target / 动态协变量 → **measurement**（FIELD）。
24
- - `timestamp` → `Time`（INT64 毫秒）；dtype 按源自适应（float32→FLOAT 等）。
25
- - 路径与原仓一致：`<子集>/<频率>/<频率>.tsfile`（无频率为 `<子集>/<子集>.tsfile`）。
26
-
27
- ## 子集索引
28
-
29
- | 子集 | 频率 | 序列数 | 观测点数 | 来源 | 引用 |
30
- |---|---|---|---|---|---|
31
- | [ETT](./ETT/README.md) | 15T, 1D, 1H, 1W | 2 | 975,520 / 10,136 / 243,880 / 1,442 | [link](https://github.com/zhouhaoyi/ETDataset) | [[1]](https://arxiv.org/abs/2012.07436) |
32
- | [LOOP_SEATTLE](./LOOP_SEATTLE/README.md) | 1D, 1H, 5T | 323 | 117,895 / 2,829,480 / 33,953,760 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
33
- | [M_DENSE](./M_DENSE/README.md) | 1D, 1H | 30 | 21,900 / 525,600 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
34
- | [SZ_TAXI](./SZ_TAXI/README.md) | 15T, 1H | 156 | 464,256 / 116,064 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
35
- | [australian_tourism](./australian_tourism/README.md) | — | 89 | 3,204 | [link](https://robjhyndman.com/publications/hierarchical-tourism/) | [[3]](https://doi.org/10.1016/j.ijforecast.2008.07.004) |
36
- | [bizitobs_l2c](./bizitobs_l2c/README.md) | 1H, 5T | 1 | 18,648 / 223,776 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
37
- | [boomlet](./boomlet/README.md) | 1062, 1209, 1225, 1230, 1282, 1487, 1631, 1676, 1855, 1975, 2187, 285, 619, 772, 963 | 1 | 344,064 / 868,352 / 802,816 / 376,832 / 573,440 / 884,736 / 418,520 / 1,046,300 / 272,012 / 392,325 / 523,100 / 1,228,800 / 851,968 / 1,097,728 / 458,752 | [link](https://huggingface.co/datasets/Datadog/BOOM) | [[5]](https://arxiv.org/abs/2505.14766) |
38
- | [ecdc_ili](./ecdc_ili/README.md) | — | 25 | 4,797 | [link](https://github.com/EU-ECDC/Respiratory_viruses_weekly_data/blob/main/data/snapshots/2025-08-08_ILIARIRates.csv) | — |
39
- | [entsoe](./entsoe/README.md) | 15T, 1H, 30T | 6 | 6,310,512 / 1,577,592 / 3,155,220 | [link](https://data.open-power-system-data.org/time_series/2020-10-06) | [[6]](https://doi.org/10.25832/time_series/2020-10-06) |
40
- | [epf_be](./epf_be/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
41
- | [epf_de](./epf_de/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
42
- | [epf_fr](./epf_fr/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
43
- | [epf_np](./epf_np/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
44
- | [epf_pjm](./epf_pjm/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
45
- | [ercot](./ercot/README.md) | 1D, 1H, 1M, 1W | 8 | 51,616 / 1,238,976 / 1,688 / 7,368 | [link](https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy) | — |
46
- | [favorita_stores](./favorita_stores/README.md) | 1D, 1M, 1W | 1,579 | 10,661,408 / 255,798 / 1,136,880 | [link](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
47
- | [favorita_transactions](./favorita_transactions/README.md) | 1D, 1M, 1W | 51 | 258,264 / 5,508 / 24,480 | [link](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
48
- | [fred_md_2025](./fred_md_2025/README.md) | — | 1 | 100,548 | [link](https://www.stlouisfed.org/research/economists/mccracken/fred-databases) | [[9]](https://doi.org/10.20955/wp.2015.012) |
49
- | [fred_qd_2025](./fred_qd_2025/README.md) | — | 1 | 65,170 | [link](https://www.stlouisfed.org/research/economists/mccracken/fred-databases) | [[10]](https://doi.org/10.20955/wp.2020.005) |
50
- | [gvar](./gvar/README.md) | — | 33 | 52,866 | [link](https://data.mendeley.com/datasets/kfp5fhgkvf/1) | [[11]](https://doi.org/10.17863/CAM.104755) |
51
- | [hermes](./hermes/README.md) | — | 10,000 | 5,220,000 | [link](https://github.com/etidav/HERMES) | [[12]](https://arxiv.org/abs/2202.03224) |
52
- | [hierarchical_sales](./hierarchical_sales/README.md) | 1D, 1W | 118 | 215,350 / 30,680 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
53
- | [hospital](./hospital/README.md) | — | 767 | 64,428 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
54
- | [hospital_admissions](./hospital_admissions/README.md) | 1D, 1W | 8 | 13,846 / 1,968 | [link](https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024) | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
55
- | [jena_weather](./jena_weather/README.md) | 10T, 1D, 1H | 1 | 1,106,784 / 7,686 / 184,464 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
56
- | [kdd_cup_2022](./kdd_cup_2022/README.md) | 10T, 1D, 30T | 134 | 47,273,860 / 325,620 / 15,755,720 | [link](https://aistudio.baidu.com/competition/detail/152/0/task-definition) | [[14]](https://arxiv.org/abs/2208.04360) |
57
- | [m5](./m5/README.md) | 1D, 1M, 1W | 30,490 | 428,849,460 / 13,805,685 / 60,857,703 | [link](https://www.kaggle.com/competitions/m5-forecasting-accuracy) | [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013) |
58
- | [proenfo_bull](./proenfo_bull/README.md) | — | 41 | 2,877,216 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
59
- | [proenfo_cockatoo](./proenfo_cockatoo/README.md) | — | 1 | 105,264 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
60
- | [proenfo_gfc12](./proenfo_gfc12/README.md) | — | 11 | 867,108 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
61
- | [proenfo_gfc14](./proenfo_gfc14/README.md) | — | 1 | 35,040 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
62
- | [proenfo_gfc17](./proenfo_gfc17/README.md) | — | 8 | 280,704 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
63
- | [proenfo_hog](./proenfo_hog/README.md) | — | 24 | 2,526,336 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
64
- | [proenfo_pdb](./proenfo_pdb/README.md) | — | 1 | 35,040 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
65
- | [redset](./redset/README.md) | 15T, 1H, 5T | 126 | 1,052,371 / 283,070 / 2,960,408 | [link](https://github.com/amazon-science/redset/) | [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet) |
66
- | [restaurant](./restaurant/README.md) | — | 817 | 294,568 | [link](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [[18]](https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/overview/citation) |
67
- | [rohlik_orders](./rohlik_orders/README.md) | 1D, 1W | 7 | 115,650 / 15,316 | [link](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge) | [[19]](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge/overview/citation) |
68
- | [rohlik_sales](./rohlik_sales/README.md) | 1D, 1W | 5,390 | 74,413,935 / 10,516,770 | [link](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2) | [[20]](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2/overview/citation) |
69
- | [rossmann](./rossmann/README.md) | 1D, 1W | 1,115 | 7,352,310 / 889,770 | [link](https://www.kaggle.com/competitions/rossmann-store-sales) | [[21]](https://www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
70
- | [solar](./solar/README.md) | 1D, 1W | 137 | 50,005 / 7,124 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
71
- | [solar_with_weather](./solar_with_weather/README.md) | 15T, 1H | 1 | 1,986,000 / 496,480 | [link](https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions) | — |
72
- | [uci_air_quality](./uci_air_quality/README.md) | 1D, 1H | 1 | 5,057 / 121,641 | [link](https://archive.ics.uci.edu/dataset/360/air+quality) | [[22]](https://doi.org/10.24432/C59K5F) |
73
- | [uk_covid_nation](./uk_covid_nation/README.md) | 1D, 1W | 4 | 41,216 / 5,936 | [link](https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed) | — |
74
- | [uk_covid_utla](./uk_covid_utla/README.md) | 1D, 1W | 214 | 308,786 / 44,448 | [link](https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed) | — |
75
- | [us_consumption](./us_consumption/README.md) | 1M, 1Q, 1Y | 31 | 24,552 / 8,122 / 1,984 | [link](https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying) | [[23]](https://doi.org/10.1016/j.ijforecast.2016.04.005) |
76
- | [walmart](./walmart/README.md) | — | 2,936 | 4,609,143 | [link](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting) | [[24]](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/overview/citation) |
77
- | [world_co2_emissions](./world_co2_emissions/README.md) | — | 191 | 11,460 | [link](https://www.kaggle.com/datasets/ulrikthygepedersen/co2-emissions-by-country) | — |
78
- | [world_life_expectancy](./world_life_expectancy/README.md) | — | 237 | 17,538 | [link](https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023) | [[25]](https://ourworldindata.org/life-expectancy#article-citation) |
79
- | [world_tourism](./world_tourism/README.md) | — | 178 | 3,738 | [link](https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact) | [[26]](https://www.worldbank.org/en/archive/using-the-archives/terms-of-use-reproduction-and-citation) |
80
-
81
- ## 读取示例
82
-
83
- ```python
84
- from tsfile import TsFileReader
85
-
86
- reader = TsFileReader("<freq>.tsfile")
87
- schemas = reader.get_all_table_schemas()
88
- # 表名：<见各子集 README>；列见下方"列含义"。
89
- ```
90
-
91
- ## 引用
92
-
93
- 原始合集 [fev-bench](https://arxiv.org/abs/2509.26468)：
94
-
95
- ```bibtex
96
- @article{shchur2025fev,
97
- title={{fev-bench}: A Realistic Benchmark for Time Series Forecasting},
98
- author={Shchur, Oleksandr and Ansari, Abdul Fatir and Turkmen, Caner and Stella, Lorenzo and Erickson, Nick and Guerron, Pablo and Bohlke-Schneider, Michael and Wang, Yuyang},
99
- year={2025},
100
- eprint={2509.26468},
101
- archivePrefix={arXiv},
102
- primaryClass={cs.LG}
103
- }
104
- ```
 
1
+ ---
2
+ license: other
3
+ task_categories:
4
+ - time-series-forecasting
5
+ tags:
6
+ - timeseries
7
+ - time-series
8
+ - tsfile
9
+ - forecasting
10
+ pretty_name: FEV datasets (TsFile format)
11
+ ---
12
+
13
+ # FEV 预测数据集合集 — TsFile 格式
14
+
15
+ 本仓库是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本，共 **49 个子集**。每个子集一个目录，含 `.tsfile` 数据文件（大表自动分片为多个 `.tsfile`）与说明 `README.md`。
16
+
17
+ > 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
18
+
19
+ ## 转换说明
20
+
21
+ - `id`（每条序列）→ TsFile **device**（TAG 维度）。
22
+ - 静态协变量列 → 也作 **TAG**（device 元数据）。
23
+ - target / 动态协变量 → **measurement**（FIELD）。
24
+ - `timestamp` → `Time`（INT64 毫秒）；dtype 按源自适应（float32→FLOAT 等）。
25
+ - 路径与原仓一致：`<子集>/<频率>/<频率>.tsfile`（无频率为 `<子集>/<子集>.tsfile`）。
26
+
27
+ ## 子集索引
28
+
29
+ | 子集 | 频率 | 序列数 | 观测点数 | 来源 | 引用 |
30
+ |---|---|---|---|---|---|
31
+ | [ETT](./ETT/README.md) | 15T, 1D, 1H, 1W | 2 | 975,520 / 10,136 / 243,880 / 1,442 | [link](https://github.com/zhouhaoyi/ETDataset) | [[1]](https://arxiv.org/abs/2012.07436) |
32
+ | [LOOP_SEATTLE](./LOOP_SEATTLE/README.md) | 1D, 1H, 5T | 323 | 117,895 / 2,829,480 / 33,953,760 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
33
+ | [M_DENSE](./M_DENSE/README.md) | 1D, 1H | 30 | 21,900 / 525,600 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
34
+ | [SZ_TAXI](./SZ_TAXI/README.md) | 15T, 1H | 156 | 464,256 / 116,064 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
35
+ | [australian_tourism](./australian_tourism/README.md) | — | 89 | 3,204 | [link](https://robjhyndman.com/publications/hierarchical-tourism/) | [[3]](https://doi.org/10.1016/j.ijforecast.2008.07.004) |
36
+ | [bizitobs_l2c](./bizitobs_l2c/README.md) | 1H, 5T | 1 | 18,648 / 223,776 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
37
+ | [boomlet](./boomlet/README.md) | 1062, 1209, 1225, 1230, 1282, 1487, 1631, 1676, 1855, 1975, 2187, 285, 619, 772, 963 | 1 | 344,064 / 868,352 / 802,816 / 376,832 / 573,440 / 884,736 / 418,520 / 1,046,300 / 272,012 / 392,325 / 523,100 / 1,228,800 / 851,968 / 1,097,728 / 458,752 | [link](https://huggingface.co/datasets/Datadog/BOOM) | [[5]](https://arxiv.org/abs/2505.14766) |
38
+ | [ecdc_ili](./ecdc_ili/README.md) | — | 25 | 4,797 | [link](https://github.com/EU-ECDC/Respiratory_viruses_weekly_data/blob/main/data/snapshots/2025-08-08_ILIARIRates.csv) | — |
39
+ | [entsoe](./entsoe/README.md) | 15T, 1H, 30T | 6 | 6,310,512 / 1,577,592 / 3,155,220 | [link](https://data.open-power-system-data.org/time_series/2020-10-06) | [[6]](https://doi.org/10.25832/time_series/2020-10-06) |
40
+ | [epf_be](./epf_be/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
41
+ | [epf_de](./epf_de/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
42
+ | [epf_fr](./epf_fr/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
43
+ | [epf_np](./epf_np/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
44
+ | [epf_pjm](./epf_pjm/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
45
+ | [ercot](./ercot/README.md) | 1D, 1H, 1M, 1W | 8 | 51,616 / 1,238,976 / 1,688 / 7,368 | [link](https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy) | — |
46
+ | [favorita_stores](./favorita_stores/README.md) | 1D, 1M, 1W | 1,579 | 10,661,408 / 255,798 / 1,136,880 | [link](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
47
+ | [favorita_transactions](./favorita_transactions/README.md) | 1D, 1M, 1W | 51 | 258,264 / 5,508 / 24,480 | [link](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
48
+ | [fred_md_2025](./fred_md_2025/README.md) | — | 1 | 100,548 | [link](https://www.stlouisfed.org/research/economists/mccracken/fred-databases) | [[9]](https://doi.org/10.20955/wp.2015.012) |
49
+ | [fred_qd_2025](./fred_qd_2025/README.md) | — | 1 | 65,170 | [link](https://www.stlouisfed.org/research/economists/mccracken/fred-databases) | [[10]](https://doi.org/10.20955/wp.2020.005) |
50
+ | [gvar](./gvar/README.md) | — | 33 | 52,866 | [link](https://data.mendeley.com/datasets/kfp5fhgkvf/1) | [[11]](https://doi.org/10.17863/CAM.104755) |
51
+ | [hermes](./hermes/README.md) | — | 10,000 | 5,220,000 | [link](https://github.com/etidav/HERMES) | [[12]](https://arxiv.org/abs/2202.03224) |
52
+ | [hierarchical_sales](./hierarchical_sales/README.md) | 1D, 1W | 118 | 215,350 / 30,680 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
53
+ | [hospital](./hospital/README.md) | — | 767 | 64,428 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
54
+ | [hospital_admissions](./hospital_admissions/README.md) | 1D, 1W | 8 | 13,846 / 1,968 | [link](https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024) | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
55
+ | [jena_weather](./jena_weather/README.md) | 10T, 1D, 1H | 1 | 1,106,784 / 7,686 / 184,464 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
56
+ | [kdd_cup_2022](./kdd_cup_2022/README.md) | 10T, 1D, 30T | 134 | 47,273,860 / 325,620 / 15,755,720 | [link](https://aistudio.baidu.com/competition/detail/152/0/task-definition) | [[14]](https://arxiv.org/abs/2208.04360) |
57
+ | [m5](./m5/README.md) | 1D, 1M, 1W | 30,490 | 428,849,460 / 13,805,685 / 60,857,703 | [link](https://www.kaggle.com/competitions/m5-forecasting-accuracy) | [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013) |
58
+ | [proenfo_bull](./proenfo_bull/README.md) | — | 41 | 2,877,216 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
59
+ | [proenfo_cockatoo](./proenfo_cockatoo/README.md) | — | 1 | 105,264 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
60
+ | [proenfo_gfc12](./proenfo_gfc12/README.md) | — | 11 | 867,108 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
61
+ | [proenfo_gfc14](./proenfo_gfc14/README.md) | — | 1 | 35,040 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
62
+ | [proenfo_gfc17](./proenfo_gfc17/README.md) | — | 8 | 280,704 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
63
+ | [proenfo_hog](./proenfo_hog/README.md) | — | 24 | 2,526,336 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
64
+ | [proenfo_pdb](./proenfo_pdb/README.md) | — | 1 | 35,040 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
65
+ | [redset](./redset/README.md) | 15T, 1H, 5T | 126 | 1,052,371 / 283,070 / 2,960,408 | [link](https://github.com/amazon-science/redset/) | [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet) |
66
+ | [restaurant](./restaurant/README.md) | — | 817 | 294,568 | [link](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [[18]](https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/overview/citation) |
67
+ | [rohlik_orders](./rohlik_orders/README.md) | 1D, 1W | 7 | 115,650 / 15,316 | [link](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge) | [[19]](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge/overview/citation) |
68
+ | [rohlik_sales](./rohlik_sales/README.md) | 1D, 1W | 5,390 | 74,413,935 / 10,516,770 | [link](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2) | [[20]](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2/overview/citation) |
69
+ | [rossmann](./rossmann/README.md) | 1D, 1W | 1,115 | 7,352,310 / 889,770 | [link](https://www.kaggle.com/competitions/rossmann-store-sales) | [[21]](https://www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
70
+ | [solar](./solar/README.md) | 1D, 1W | 137 | 50,005 / 7,124 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
71
+ | [solar_with_weather](./solar_with_weather/README.md) | 15T, 1H | 1 | 1,986,000 / 496,480 | [link](https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions) | — |
72
+ | [uci_air_quality](./uci_air_quality/README.md) | 1D, 1H | 1 | 5,057 / 121,641 | [link](https://archive.ics.uci.edu/dataset/360/air+quality) | [[22]](https://doi.org/10.24432/C59K5F) |
73
+ | [uk_covid_nation](./uk_covid_nation/README.md) | 1D, 1W | 4 | 41,216 / 5,936 | [link](https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed) | — |
74
+ | [uk_covid_utla](./uk_covid_utla/README.md) | 1D, 1W | 214 | 308,786 / 44,448 | [link](https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed) | — |
75
+ | [us_consumption](./us_consumption/README.md) | 1M, 1Q, 1Y | 31 | 24,552 / 8,122 / 1,984 | [link](https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying) | [[23]](https://doi.org/10.1016/j.ijforecast.2016.04.005) |
76
+ | [walmart](./walmart/README.md) | — | 2,936 | 4,609,143 | [link](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting) | [[24]](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/overview/citation) |
77
+ | [world_co2_emissions](./world_co2_emissions/README.md) | — | 191 | 11,460 | [link](https://www.kaggle.com/datasets/ulrikthygepedersen/co2-emissions-by-country) | — |
78
+ | [world_life_expectancy](./world_life_expectancy/README.md) | — | 237 | 17,538 | [link](https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023) | [[25]](https://ourworldindata.org/life-expectancy#article-citation) |
79
+ | [world_tourism](./world_tourism/README.md) | — | 178 | 3,738 | [link](https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact) | [[26]](https://www.worldbank.org/en/archive/using-the-archives/terms-of-use-reproduction-and-citation) |
80
+
81
+ ## 读取示例
82
+
83
+ ```python
84
+ from tsfile import TsFileReader
85
+
86
+ reader = TsFileReader("<freq>.tsfile")
87
+ schemas = reader.get_all_table_schemas()
88
+ # 表名：<见各子集 README>；列见下方"列含义"。
89
+ ```
90
+
91
+ ## 引用
92
+
93
+ 原始合集 [fev-bench](https://arxiv.org/abs/2509.26468)：
94
+
95
+ ```bibtex
96
+ @article{shchur2025fev,
97
+ title={{fev-bench}: A Realistic Benchmark for Time Series Forecasting},
98
+ author={Shchur, Oleksandr and Ansari, Abdul Fatir and Turkmen, Caner and Stella, Lorenzo and Erickson, Nick and Guerron, Pablo and Bohlke-Schneider, Michael and Wang, Yuyang},
99
+ year={2025},
100
+ eprint={2509.26468},
101
+ archivePrefix={arXiv},
102
+ primaryClass={cs.LG}
103
+ }
104
+ ```