> ## Documentation Index > Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > Download and wrangling utility for long-horizon datasets. These datasets have been used by `NHITS, AutoFormer, Informer, PatchTST, TiDE` among many other neural forecasting methods. The datasets include the original [ETTh1, ETTh2, ETTm1, ETTm2, Weather, ILI, TrafficL](https://github.com/zhouhaoyi/ETDataset) benchmark datasets. # Long-Horizon Original Datasets *** source ### Weather > ```text theme={null} > Weather (freq:str='10M', name:str='weather', n_ts:int=21, > test_size:int=10539, val_size:int=5270, horizons:Tuple[int]=(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. [https://arxiv.org/abs/2106.13008.\\](https://arxiv.org/abs/2106.13008.\\)\* *** source ### TrafficL > ```text theme={null} > TrafficL (freq:str='H', name:str='traffic', n_ts:int=862, > test_size:int=3508, val_size:int=1756, horizons:Tuple[int]=(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: Lai, G., Chang, W., Yang, Y., and Liu, H. Modeling Long and Short-Term Temporal Patterns with Deep Neural Networks. SIGIR 2018. [http://arxiv.org/abs/1703.07015](http://arxiv.org/abs/1703.07015). Wu, H., Xu, J., Wang, J., and Long, M. Autoformer: Decomposition Transformers with auto-correlation for long-term series forecasting. NeurIPS 2021. [https://arxiv.org/abs/2106.13008.\\](https://arxiv.org/abs/2106.13008.\\)\* *** source ### ECL > ```text theme={null} > ECL (freq:str='15T', name:str='ECL', n_ts:int=321, n_time:int=26304, > test_size:int=5260, val_size:int=2632, horizons:Tuple[int]=(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. [http://arxiv.org/abs/1907.00235.\\](http://arxiv.org/abs/1907.00235.\\)\* *** source ### ETTm2 > ```text theme={null} > ETTm2 (freq:str='15T', name:str='ETTm2', n_ts:int=7, n_time:int=57600, > test_size:int=11520, val_size:int=11520, horizons:Tuple[int]=(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. [https://arxiv.org/abs/2012.07436\\](https://arxiv.org/abs/2012.07436\\)\* *** source ### ETTm1 > ```text theme={null} > ETTm1 (freq:str='15T', name:str='ETTm1', n_ts:int=7, n_time:int=57600, > test_size:int=11520, val_size:int=11520, horizons:Tuple[int]=(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.* *** source ### ETTh2 > ```text theme={null} > ETTh2 (freq:str='H', name:str='ETTh2', n_ts:int=7, n_time:int=14400, > test_size:int=2880, val_size:int=2880, horizons:Tuple[int]=(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.* *** source ### ETTh1 > ```text theme={null} > ETTh1 (freq:str='H', name:str='ETTh1', n_ts:int=7, n_time:int=14400, > test_size:int=2880, val_size:int=2880, horizons:Tuple[int]=(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.* *** source ### LongHorizon2 > ```text theme={null} > LongHorizon2 (source_url:str='https://www.dropbox.com/s/rlc1qmprpvuqrsv/a > ll_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.\*