Dataset Viewer
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2022-04-02 00:00:00
| 49.208571 |
instance_1
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2022-04-02 00:10:00
| 70.747 |
instance_1
|
2022-04-02 00:20:00
| 61.513 |
instance_1
|
2022-04-02 00:30:00
| 58.166 |
instance_1
|
2022-04-02 00:40:00
| 28.039 |
instance_1
|
2022-04-02 00:50:00
| 41.813 |
instance_1
|
2022-04-02 01:00:00
| 34.396 |
instance_1
|
2022-04-02 01:10:00
| 34.249 |
instance_1
|
2022-04-02 01:20:00
| 24.966 |
instance_1
|
2022-04-02 01:30:00
| 39.493 |
instance_1
|
2022-04-02 01:40:00
| 38.346 |
instance_1
|
2022-04-02 01:50:00
| 23.126 |
instance_1
|
2022-04-02 02:00:00
| 32.234 |
instance_1
|
2022-04-02 02:10:00
| 42.015 |
instance_1
|
2022-04-02 02:20:00
| 24.355 |
instance_1
|
2022-04-02 02:30:00
| 68.711 |
instance_1
|
2022-04-02 02:40:00
| 24.974 |
instance_1
|
2022-04-02 02:50:00
| 43.079 |
instance_1
|
2022-04-02 03:00:00
| 59.869 |
instance_1
|
2022-04-02 03:10:00
| 41.194 |
instance_1
|
2022-04-02 03:20:00
| 30.119 |
instance_1
|
2022-04-02 03:30:00
| 36.287 |
instance_1
|
2022-04-02 03:40:00
| 64.412 |
instance_1
|
2022-04-02 03:50:00
| 50.318 |
instance_1
|
2022-04-02 04:00:00
| 64.747 |
instance_1
|
2022-04-02 04:10:00
| 101.668 |
instance_1
|
2022-04-02 04:20:00
| 53.276 |
instance_1
|
2022-04-02 04:30:00
| 74.592 |
instance_1
|
2022-04-02 04:40:00
| 53.33 |
instance_1
|
2022-04-02 04:50:00
| 108.756 |
instance_1
|
2022-04-02 05:00:00
| 78.965 |
instance_1
|
2022-04-02 05:10:00
| 81.751 |
instance_1
|
2022-04-02 05:20:00
| 131.363 |
instance_1
|
2022-04-02 05:30:00
| 114.222 |
instance_1
|
2022-04-02 05:40:00
| 108.253 |
instance_1
|
2022-04-02 05:50:00
| 132.338 |
instance_1
|
2022-04-02 06:00:00
| 110.436 |
instance_1
|
2022-04-02 06:10:00
| 121.502 |
instance_1
|
2022-04-02 06:20:00
| 103.692 |
instance_1
|
2022-04-02 06:30:00
| 131.779 |
instance_1
|
2022-04-02 06:40:00
| 143.8 |
instance_1
|
2022-04-02 06:50:00
| 106.81 |
instance_1
|
2022-04-02 07:00:00
| 125.367 |
instance_1
|
2022-04-02 07:10:00
| 118.018 |
instance_1
|
2022-04-02 07:20:00
| 144.048 |
instance_1
|
2022-04-02 07:30:00
| 122.593 |
instance_1
|
2022-04-02 07:40:00
| 142.376 |
instance_1
|
2022-04-02 07:50:00
| 91.864 |
instance_1
|
2022-04-02 08:00:00
| 99.905 |
instance_1
|
2022-04-02 08:10:00
| 100.677 |
instance_1
|
2022-04-02 08:20:00
| 113.673 |
instance_1
|
2022-04-02 08:30:00
| 99.811 |
instance_1
|
2022-04-02 08:40:00
| 140.867 |
instance_1
|
2022-04-02 08:50:00
| 130.704 |
instance_1
|
2022-04-02 09:00:00
| 146.682 |
instance_1
|
2022-04-02 09:10:00
| 175.313 |
instance_1
|
2022-04-02 09:20:00
| 177.508 |
instance_1
|
2022-04-02 09:30:00
| 152.638 |
instance_1
|
2022-04-02 09:40:00
| 176.807 |
instance_1
|
2022-04-02 09:50:00
| 142.397 |
instance_1
|
2022-04-02 10:00:00
| 181.889 |
instance_1
|
2022-04-02 10:10:00
| 188.989 |
instance_1
|
2022-04-02 10:20:00
| 114.089 |
instance_1
|
2022-04-02 10:30:00
| 147.665 |
instance_1
|
2022-04-02 10:40:00
| 146.992 |
instance_1
|
2022-04-02 10:50:00
| 124.409 |
instance_1
|
2022-04-02 11:00:00
| 177.513 |
instance_1
|
2022-04-02 11:10:00
| 138.818 |
instance_1
|
2022-04-02 11:20:00
| 157.481 |
instance_1
|
2022-04-02 11:30:00
| 134.153 |
instance_1
|
2022-04-02 11:40:00
| 178.046 |
instance_1
|
2022-04-02 11:50:00
| 171.142 |
instance_1
|
2022-04-02 12:00:00
| 142.77 |
instance_1
|
2022-04-02 12:10:00
| 141.939 |
instance_1
|
2022-04-02 12:20:00
| 137.476 |
instance_1
|
2022-04-02 12:30:00
| 150.931 |
instance_1
|
2022-04-02 12:40:00
| 124.092 |
instance_1
|
2022-04-02 12:50:00
| 175.458 |
instance_1
|
2022-04-02 13:00:00
| 162.949 |
instance_1
|
2022-04-02 13:10:00
| 165.198 |
instance_1
|
2022-04-02 13:20:00
| 151.664 |
instance_1
|
2022-04-02 13:30:00
| 169.18 |
instance_1
|
2022-04-02 13:40:00
| 103.296 |
instance_1
|
2022-04-02 13:50:00
| 154.559 |
instance_1
|
2022-04-02 14:00:00
| 88.017 |
instance_1
|
2022-04-02 14:10:00
| 71.681 |
instance_1
|
2022-04-02 14:20:00
| 143.739 |
instance_1
|
2022-04-02 14:30:00
| 113.41 |
instance_1
|
2022-04-02 14:40:00
| 131.731 |
instance_1
|
2022-04-02 14:50:00
| 102.827 |
instance_1
|
2022-04-02 15:00:00
| 114.933 |
instance_1
|
2022-04-02 15:10:00
| 106.082 |
instance_1
|
2022-04-02 15:20:00
| 123.449 |
instance_1
|
2022-04-02 15:30:00
| 114.388 |
instance_1
|
2022-04-02 15:40:00
| 93.306 |
instance_1
|
2022-04-02 15:50:00
| 96.937 |
instance_1
|
2022-04-02 16:00:00
| 90.742 |
instance_1
|
2022-04-02 16:10:00
| 108.942 |
instance_1
|
2022-04-02 16:20:00
| 128.147 |
instance_1
|
2022-04-02 16:30:00
| 135.037 |
instance_1
|
End of preview. Expand
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YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
Intro
The data organization follows TFB format: https://github.com/decisionintelligence/TFB.
TFB data format
TFB stores time series in a format of three column long tables, which we will introduce below:
Format Introduction
First column: date (the exact column name is required, the same applies below.)
- The columns stores the time information in the time series, which can be in either of the following formats:
- Timestamps in string, datetime, or other types that are compatible with pd.to_datetime;
- Integers starting from 1, e.g. 1, 2, 3, 4, 5, ...
Second column: data
- This column stores the series values corresponding to the timestamps.
Third column: cols
- This column stores the column name (variable name).
Multivariate time series example:
A common time series in wide table format:
date | channel1 | channel2 | channel3 |
---|---|---|---|
1 | 0.1 | 1 | 10 |
2 | 0.2 | 2 | 20 |
3 | 0.3 | 3 | 30 |
Convert to TFB format:
date | data | cols |
---|---|---|
1 | 0.1 | channel1 |
2 | 0.2 | channel1 |
3 | 0.3 | channel1 |
1 | 1 | channel2 |
2 | 2 | channel2 |
3 | 3 | channel2 |
1 | 10 | channel3 |
2 | 20 | channel3 |
3 | 30 | channel3 |
License
This dataset is released under CC BY 4.0
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