pip install -r requirements.txt| ID | Domain | Data Structure | File Format | Data Points | Dimensions | Time interval |
|---|---|---|---|---|---|---|
| 0 | Windspeed | (-/-/-) | csv | 105,119 | 51 | 5min |
| 1 | Electricity | (-/-/-) | csv | 105,119 | 31 | 5min |
| 2 | Air Quality | (+/+/+) | csv | 43,824 | 12 | 1h |
| 3 | Electricity | (+/+/+) | csv | 2,075,259 | 8 | 1min |
| 4 | Air Quality | (+/+/+) | xlsx | 9,471 | 16 | 1h |
| 5 | Air Quality | (+/+/+) | csv | 2,891,393 | 7 | 1h |
| 6 | Traffic | (+/o/-) | txt | 3,997,413 | 11 | 1h |
| 7 | Crime | (+/+/+) | csv | 2,678,959 | 15 | irregular |
| 8 | Weather | (+/+/+) | txt | 2,764 | 24 | 15min |
| 9 | Ozone Level | (+/o/+) | csv | 2,536 | 74 | 1h |
| 10 | Fertility | (+/+/+) | rda | 574 | 4 | 1yr |
| 11 | Mortality | (+/+/+) | csv | 21,201 | 8 | 1yr |
| 12 | Weather, Bike-Sharing | (+/+/+) | csv | 731 | 15 | 1d |
| 13 | Weather, Bike-Sharing | (+/+/+) | csv | 17,379 | 16 | 1h |
| 14 | Electricity, Weather | (+/+/+) | xlsx | 713 | 3 | 1d |
| 15 | Weather | (+/+/+) | xlsx | 15,072 | 12 | 1h |
| 16 | Machine Sensor | (-/o/-) | txt | - | - | 100ms |
| 17 | AD Exchange Rate | (+/o/+) | csv | 9,610 | 3 | 1h |
| 18 | Multiple | (+/o/+) | csv | 69,561 | 3 | 5min |
| 19 | Traffic | (+/o/+) | csv | 15,664 | 3 | 5min |
| 20 | Cloud Load | (+/o/+) | csv | 67,740 | 3 | 5min |
| 21 | Tweet Count | (+/o/+) | csv | 158,631 | 3 | 5min |
| 22 | Synthetic | (+/+/-) | mat | - | - | |
| 23 | Electricity | (+/-/-) | txt | 140,256 | 370 | 15min |
| 24 | Exchange Rate | (+/-/-) | txt | 7,587 | 7 | 1d |
| 25 | Traffic | (+/-/-) | txt | 17,543 | 861 | 1h |
| 26 | Solar | (+/-/-) | txt | 52,559 | 136 | 10min |
| 27 | Weather | (+/+/+) | csv | - | - | 1min |
| 28 | Water Level | (+/+/+) | xlsx | 36,160 | 4 | 1d |
| 29 | Air Quality | (+/+/+) | csv | 420,768 | 19 | 15min |
| 30 | Air Quality | (+/+/+) | csv | 79,559 | 11 | 15min |
| 31 | Crime | (+/+/+) | csv | 2,129,525 | 34 | 1min |
| 32 | Chemicals | (+/+/+) | xlsx | 120,630 | 7 | 1min |
| 33 | Multiple | (+/-/-) | txt | 71 | 110 | 1 M. |
| 34 | Multiple | (+/+/+) | txt | 167,562 | 3 | 1yr, 1q, 1m |
| 35 | Traffic | (+/+/-) | xls | - | - | 1d |
| 36 | Tourism | (+/-/-) | csv | 309 | 794 | 1m 1q |
| 37 | Web Traffic | (+/+/+) | csv | 290,126 | 804 | 1d |
| 38 | Multiple | (+/o/+) | csv | 414 | 960 | 1yr, 1q, 1m, 1w, 1d, 1h |
| 39 | Machine Sensor | (+/+/+) | csv | 34,840 | 9 | 1h, 1m |
| 40 | Synthetic | (-/-/-) | pickle | - | - | |
| 41 | Electricity | (+/+/+) | csv | 4,055,880 | 6 | 5min, 1h |
| 42 | Weather | (+/+/+) | csv | 633,494,597 | 125 | 1yr |
| 43 | Electricity | (+/-/+) | csv | 257,896 | 27 | 1h |
| 44 | Trajectory | (+/+/+) | txt | 8,241,680 | 14 | 1s |
| 45 | Wind | (+/+/-) | csv | 262,968 | 254 | hourly |
| 46 | Bike-Usage | (+/+/+) | csv | 52,584 | 5 | hourly |
| 47 | Electricity | (+/+/+) | csv | 48,048 | 16 | hourly |
| 48 | Illness | (+/+/+) | csv | 966 | 7 | weekly |
| 49 | Sales | (+/+/+) | csv | 1,058,297 | 9 | daily |
| 50 | Weather | (+/+/+) | csv | 52,696 | 21 | 10min |
| 51 | Traffic | (+/-/-) | mat | 57,636 | 48 | hourly |
| 52 | Weather | (+/+/+) | csv | 35,064 | 12 | hourly |
python compute_stat_measurements.py --config-file "data/example_config.json" --create-cleaned-version --compute-statspython compute_stat_measurements.py --config-file "data/example_config.json" --create-cleaned-version --compute-mpdist{
"ds_0": {
"__file__": "ad_exchange.csv",
"sort": "event",
"Forecasting Values": ["value"]
},
"ds_1": {
"__file__": "WTH.csv",
"sort": "",
"Forecasting Values": ["wetbulbcelsius"]
}
}@article{hahn2023time,
title={Time Series Dataset Survey for Forecasting with Deep Learning},
author={Hahn, Yannik and Langer, Tristan and Meyes, Richard and Meisen, Tobias},
journal={Forecasting},
volume={5},
number={1},
pages={315--335},
year={2023},
publisher={MDPI}
}
