Datasets:
metadata
annotations_creators:
- no-annotation
license: other
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
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path: ETTh/train-*
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path: LOOP_SEATTLE/1D/train-*
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path: LOOP_SEATTLE/1H/train-*
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path: LOOP_SEATTLE/5T/train-*
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path: M_DENSE/1D/train-*
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path: M_DENSE/1H/train-*
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path: SZ_TAXI/1H/train-*
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path: beijing_air_quality/train-*
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- config_name: kdd_cup_2018_1H
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- config_name: kdd_cup_2022_10T
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- config_name: kdd_cup_2022_1D
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path: kdd_cup_2022/1D/train-*
- config_name: kdd_cup_2022_30T
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path: kdd_cup_2022/30T/train-*
- config_name: m5_1D
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path: m5/1D/train-*
- config_name: m5_1M
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path: m5/1M/train-*
- config_name: m5_1W
data_files:
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- config_name: proenfo_bull
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Forecast evaluation datasets
This repository contains time series datasets that can be used for evaluation of univariate & multivariate forecasting models.
The main focus of this repository is on datasets that reflect real-world forecasting scenarios, such as those involving covariates, missing values, and other practical complexities.
The datasets follow a format that is compatible with the fev package.
Data format and usage
Each dataset satisfies the following schema:
- each dataset entry (=row) represents a single univariate or multivariate time series
- each entry contains
- 1/ a field of type
Sequence(timestamp)that contains the timestamps of observations - 2/ at least one field of type
Sequence(float)that can be used as the target time series or dynamic covariates - 3/ a field of type
stringthat contains the unique ID of each time series
- 1/ a field of type
- all fields of type
Sequencehave the same length
Datasets can be loaded using the 🤗 datasets library.
import datasets
ds = datasets.load_dataset("autogluon/fev_datasets", "epf_electricity_de", split="train")
ds.set_format("numpy") # sequences returned as numpy arrays
Example entry in the epf_electricity_de dataset
>>> ds[0]
{'id': 'DE',
'timestamp': array(['2012-01-09T00:00:00.000000', '2012-01-09T01:00:00.000000',
'2012-01-09T02:00:00.000000', ..., '2017-12-31T21:00:00.000000',
'2017-12-31T22:00:00.000000', '2017-12-31T23:00:00.000000'],
dtype='datetime64[us]'),
'target': array([34.97, 33.43, 32.74, ..., 5.3 , 1.86, -0.92], dtype=float32),
'Ampirion Load Forecast': array([16382. , 15410.5, 15595. , ..., 15715. , 15876. , 15130. ],
dtype=float32),
'PV+Wind Forecast': array([ 3569.5276, 3315.275 , 3107.3076, ..., 29653.008 , 29520.33 ,
29466.408 ], dtype=float32)}
For more details about the dataset format and usage, check out the fev documentation on GitHub.
Dataset statistics
Disclaimer: These datasets have been converted into a unified format from external sources. Please refer to the original sources for licensing and citation terms. We do not claim any rights to the original data. Unless otherwise specified, the datasets are provided only for research purposes.