Long-Horizon Original Datasets

`Weather`

Weather(freq='10M', name='weather', n_ts=21, test_size=10539, val_size=5270, horizons=(96, 192, 336, 720))

This Weather dataset contains the 2020 year of 21 meteorological measurements recorded every 10 minutes from the Weather Station of the Max Planck Biogeochemistry Institute in Jena, Germany. Reference:

Wu, H., Xu, J., Wang, J., and Long, M. Autoformer: Decomposition Transformers with auto-correlation for long-term series forecasting. NeurIPS 2021.

`TrafficL`

TrafficL(freq='H', name='traffic', n_ts=862, test_size=3508, val_size=1756, horizons=(96, 192, 336, 720))

This large Traffic dataset was collected by the California Department of Transportation, it reports road hourly occupancy rates of 862 sensors, from January 2015 to December 2016. Reference:

`ECL`

ECL(freq='15T', name='ECL', n_ts=321, n_time=26304, test_size=5260, val_size=2632, horizons=(96, 192, 336, 720))

The Electricity dataset reports the fifteen minute electricity consumption (KWh) of 321 customers from 2012 to 2014. For comparability, we aggregate it hourly. Reference:

Li, S et al. Enhancing the locality and breaking the memory bottleneck of Transformer on time series forecasting. NeurIPS 2019.

`ETTm2`

ETTm2(freq='15T', name='ETTm2', n_ts=7, n_time=57600, test_size=11520, val_size=11520, horizons=(96, 192, 336, 720))

The ETTm2 dataset monitors an electricity transformer from a region of a province of China including oil temperature and variants of load (such as high useful load and high useless load) from July 2016 to July 2018 at a fifteen minute frequency. Reference:

Zhou, et al. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI 2021.

`ETTm1`

ETTm1(freq='15T', name='ETTm1', n_ts=7, n_time=57600, test_size=11520, val_size=11520, horizons=(96, 192, 336, 720))

The ETTm1 dataset monitors an electricity transformer from a region of a province of China including oil temperature and variants of load (such as high useful load and high useless load) from July 2016 to July 2018 at a fifteen minute frequency.

`ETTh2`

ETTh2(freq='H', name='ETTh2', n_ts=7, n_time=14400, test_size=2880, val_size=2880, horizons=(96, 192, 336, 720))

The ETTh2 dataset monitors an electricity transformer from a region of a province of China including oil temperature and variants of load (such as high useful load and high useless load) from July 2016 to July 2018 at an hourly frequency.

`ETTh1`

ETTh1(freq='H', name='ETTh1', n_ts=7, n_time=14400, test_size=2880, val_size=2880, horizons=(96, 192, 336, 720))

The ETTh1 dataset monitors an electricity transformer from a region of a province of China including oil temperature and variants of load (such as high useful load and high useless load) from July 2016 to July 2018 at an hourly frequency.

`LongHorizon2`

LongHorizon2(source_url='https://www.dropbox.com/s/rlc1qmprpvuqrsv/all_six_datasets.zip?dl=1')

This Long-Horizon datasets wrapper class, provides with utility to download and wrangle the following datasets: ETT, ECL, Exchange, Traffic, ILI and Weather.

Each set is normalized with the train data mean and standard deviation.
Datasets are partitioned into train, validation and test splits.
For all datasets: 70%, 10%, and 20% of observations are train, validation, test, except ETT that uses 20% validation.

`LongHorizon2.download`

download(directory)

Download Long Horizon 2 Datasets. Parameters:

Name	Type	Description	Default
`directory`	`str`	Directory path to download dataset.	required

`LongHorizon2.load`

load(directory, group, normalize=True)

Downloads and long-horizon forecasting benchmark datasets. Parameters:

Name	Type	Description	Default
`directory`	`str`	Directory where data will be downloaded.	required
`group`	`str`	Group name. Allowed groups: ‘ETTh1’, ‘ETTh2’, ‘ETTm1’, ‘ETTm2’, ‘ECL’, ‘Exchange’, ‘Traffic’, ‘Weather’, ‘ILI’.	required
`normalize`	`bool`	If `True` std. normalize data or not	`True`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Target time series with columns [‘unique_id’, ‘ds’, ‘y’].

API Reference

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​Weather

​TrafficL

​ECL

​ETTm2

​ETTm1

​ETTh2

​ETTh1

​LongHorizon2

​LongHorizon2.download

​LongHorizon2.load