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
pretty_name: Chronos datasets
dataset_info:
- config_name: dominick
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: im_0
dtype: int64
splits:
- name: train
num_bytes: 477140250
num_examples: 100014
download_size: 60199910
dataset_size: 477140250
homepage: https://www.chicagobooth.edu/research/kilts/research-data/dominicks
- config_name: electricity_15min
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: consumption_kW
sequence: float64
splits:
- name: train
num_bytes: 670989988
num_examples: 370
download_size: 284497403
dataset_size: 670989988
license: CC BY 4.0
homepage: https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014
- config_name: ercot
features:
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dtype: string
- name: timestamp
sequence: timestamp[ns]
- name: COAST
sequence: float64
- name: EAST
sequence: float64
- name: FAR_WEST
sequence: float64
- name: NORTH
sequence: float64
- name: NORTH_C
sequence: float64
- name: SOUTHERN
sequence: float64
- name: SOUTH_C
sequence: float64
- name: WEST
sequence: float64
splits:
- name: train
num_bytes: 11150831
num_examples: 1
download_size: 8467903
dataset_size: 11150831
- config_name: exchange_rate
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: currency_1
sequence: float64
- name: currency_2
sequence: float64
- name: currency_3
sequence: float64
- name: currency_4
sequence: float64
- name: currency_5
sequence: float64
- name: currency_6
sequence: float64
- name: currency_7
sequence: float64
- name: currency_8
sequence: float64
splits:
- name: train
num_bytes: 546383
num_examples: 1
download_size: 455892
dataset_size: 546383
license: MIT
homepage: >-
https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate
- config_name: m4_daily
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: category
dtype: string
splits:
- name: train
num_bytes: 160504176
num_examples: 4227
download_size: 65546675
dataset_size: 160504176
homepage: https://github.com/Mcompetitions/M4-methods
- config_name: m4_hourly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: category
dtype: string
splits:
- name: train
num_bytes: 5985544
num_examples: 414
download_size: 1336971
dataset_size: 5985544
homepage: https://github.com/Mcompetitions/M4-methods
- config_name: m4_monthly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: category
dtype: string
splits:
- name: train
num_bytes: 181372969
num_examples: 48000
download_size: 52772258
dataset_size: 181372969
homepage: https://github.com/Mcompetitions/M4-methods
- config_name: m4_quarterly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: category
dtype: string
splits:
- name: train
num_bytes: 39205397
num_examples: 24000
download_size: 13422579
dataset_size: 39205397
homepage: https://github.com/Mcompetitions/M4-methods
- config_name: m4_weekly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: category
dtype: string
splits:
- name: train
num_bytes: 5955806
num_examples: 359
download_size: 2556691
dataset_size: 5955806
homepage: https://github.com/Mcompetitions/M4-methods
- config_name: m4_yearly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: category
dtype: string
splits:
- name: train
num_bytes: 14410042
num_examples: 23000
download_size: 5488601
dataset_size: 14410042
homepage: https://github.com/Mcompetitions/M4-methods
- config_name: m5
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: item_id
dtype: string
- name: target
sequence: float32
- name: dept_id
dtype: string
- name: cat_id
dtype: string
- name: store_id
dtype: string
- name: state_id
dtype: string
splits:
- name: train
num_bytes: 574062630
num_examples: 30490
download_size: 78063286
dataset_size: 574062630
homepage: https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules
- config_name: mexico_city_bikes
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 618999406
num_examples: 494
download_size: 103206946
dataset_size: 618999406
homepage: https://ecobici.cdmx.gob.mx/en/open-data/
- config_name: monash_australian_electricity
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 18484319
num_examples: 5
download_size: 16856156
dataset_size: 18484319
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_car_parts
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 2232790
num_examples: 2674
download_size: 70278
dataset_size: 2232790
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_cif_2016
features:
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- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 115096
num_examples: 72
download_size: 70876
dataset_size: 115096
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_covid_deaths
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 907326
num_examples: 266
download_size: 58957
dataset_size: 907326
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_electricity_hourly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 135103443
num_examples: 321
download_size: 31139117
dataset_size: 135103443
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_electricity_weekly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 807315
num_examples: 321
download_size: 333563
dataset_size: 807315
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_fred_md
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 1248369
num_examples: 107
download_size: 412207
dataset_size: 1248369
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_hospital
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license: CC BY 4.0
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_london_smart_meters
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m1_quarterly
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
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path: m4_hourly/train-*
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path: m4_weekly/train-*
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path: m5/train-*
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path: mexico_city_bikes/train-*
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- split: train
path: monash_australian_electricity/train-*
- config_name: monash_car_parts
data_files:
- split: train
path: monash_car_parts/train-*
- config_name: monash_cif_2016
data_files:
- split: train
path: monash_cif_2016/train-*
- config_name: monash_covid_deaths
data_files:
- split: train
path: monash_covid_deaths/train-*
- config_name: monash_electricity_hourly
data_files:
- split: train
path: monash_electricity_hourly/train-*
- config_name: monash_electricity_weekly
data_files:
- split: train
path: monash_electricity_weekly/train-*
- config_name: monash_fred_md
data_files:
- split: train
path: monash_fred_md/train-*
- config_name: monash_hospital
data_files:
- split: train
path: monash_hospital/train-*
- config_name: monash_kdd_cup_2018
data_files:
- split: train
path: monash_kdd_cup_2018/train-*
- config_name: monash_london_smart_meters
data_files:
- split: train
path: monash_london_smart_meters/train-*
- config_name: monash_m1_monthly
data_files:
- split: train
path: monash_m1_monthly/train-*
- config_name: monash_m1_quarterly
data_files:
- split: train
path: monash_m1_quarterly/train-*
- config_name: monash_m1_yearly
data_files:
- split: train
path: monash_m1_yearly/train-*
- config_name: monash_m3_monthly
data_files:
- split: train
path: monash_m3_monthly/train-*
- config_name: monash_m3_quarterly
data_files:
- split: train
path: monash_m3_quarterly/train-*
- config_name: monash_m3_yearly
data_files:
- split: train
path: monash_m3_yearly/train-*
- config_name: monash_nn5_weekly
data_files:
- split: train
path: monash_nn5_weekly/train-*
- config_name: monash_pedestrian_counts
data_files:
- split: train
path: monash_pedestrian_counts/train-*
- config_name: monash_rideshare
data_files:
- split: train
path: monash_rideshare/train-*
- config_name: monash_saugeenday
data_files:
- split: train
path: monash_saugeenday/train-*
- config_name: monash_temperature_rain
data_files:
- split: train
path: monash_temperature_rain/train-*
- config_name: monash_tourism_monthly
data_files:
- split: train
path: monash_tourism_monthly/train-*
- config_name: monash_tourism_quarterly
data_files:
- split: train
path: monash_tourism_quarterly/train-*
- config_name: monash_tourism_yearly
data_files:
- split: train
path: monash_tourism_yearly/train-*
- config_name: monash_traffic
data_files:
- split: train
path: monash_traffic/train-*
- config_name: monash_weather
data_files:
- split: train
path: monash_weather/train-*
- config_name: nn5
data_files:
- split: train
path: nn5/train-*
- config_name: solar
data_files:
- split: train
path: solar/train-*
- config_name: solar_1h
data_files:
- split: train
path: solar_1h/train-*
- config_name: taxi_1h
data_files:
- split: train
path: taxi_1h/train-*
- config_name: taxi_30min
data_files:
- split: train
path: taxi_30min/train-*
- config_name: uber_tlc_daily
data_files:
- split: train
path: uber_tlc_daily/train-*
- config_name: uber_tlc_hourly
data_files:
- split: train
path: uber_tlc_hourly/train-*
- config_name: ushcn_daily
data_files:
- split: train
path: ushcn_daily/train-*
- config_name: weatherbench_daily
data_files:
- split: train
path: weatherbench_daily/train-*
- config_name: weatherbench_hourly_10m_u_component_of_wind
data_files:
- split: train
path: weatherbench_hourly/10m_u_component_of_wind/train-*
- config_name: weatherbench_hourly_10m_v_component_of_wind
data_files:
- split: train
path: weatherbench_hourly/10m_v_component_of_wind/train-*
- config_name: weatherbench_hourly_2m_temperature
data_files:
- split: train
path: weatherbench_hourly/2m_temperature/train-*
- config_name: weatherbench_hourly_geopotential
data_files:
- split: train
path: weatherbench_hourly/geopotential/train-*
- config_name: weatherbench_hourly_potential_vorticity
data_files:
- split: train
path: weatherbench_hourly/potential_vorticity/train-*
- config_name: weatherbench_hourly_relative_humidity
data_files:
- split: train
path: weatherbench_hourly/relative_humidity/train-*
- config_name: weatherbench_hourly_specific_humidity
data_files:
- split: train
path: weatherbench_hourly/specific_humidity/train-*
- config_name: weatherbench_hourly_temperature
data_files:
- split: train
path: weatherbench_hourly/temperature/train-*
- config_name: weatherbench_hourly_toa_incident_solar_radiation
data_files:
- split: train
path: weatherbench_hourly/toa_incident_solar_radiation/train-*
- config_name: weatherbench_hourly_total_cloud_cover
data_files:
- split: train
path: weatherbench_hourly/total_cloud_cover/train-*
- config_name: weatherbench_hourly_total_precipitation
data_files:
- split: train
path: weatherbench_hourly/total_precipitation/train-*
- config_name: weatherbench_hourly_u_component_of_wind
data_files:
- split: train
path: weatherbench_hourly/u_component_of_wind/train-*
- config_name: weatherbench_hourly_v_component_of_wind
data_files:
- split: train
path: weatherbench_hourly/v_component_of_wind/train-*
- config_name: weatherbench_hourly_vorticity
data_files:
- split: train
path: weatherbench_hourly/vorticity/train-*
- config_name: weatherbench_weekly
data_files:
- split: train
path: weatherbench_weekly/train-*
- config_name: wiki_daily_100k
data_files:
- split: train
path: wiki_daily_100k/train-*
- config_name: wind_farms_daily
data_files:
- split: train
path: wind_farms_daily/train-*
- config_name: wind_farms_hourly
data_files:
- split: train
path: wind_farms_hourly/train-*
Chronos datasets
Time series datasets used for training and evaluation of the Chronos forecasting models.
Note that some Chronos datasets (ETTh, ETTm, brazilian_cities_temperature and spanish_energy_and_weather) that rely on a custom builder script are available in the companion repo autogluon/chronos_datasets_extra.
See the paper for more information.
Data format and usage
All datasets satisfy the following high-level schema:
- Each dataset row corresponds to a single (univariate or multivariate) time series.
- There exists one column with name
idand typestringthat contains the unique identifier of each time series. - There exists one column of type
Sequencewith dtypetimestamp[ms]. This column contains the timestamps of the observations. Timestamps are guaranteed to have a regular frequency that can be obtained withpandas.infer_freq. - There exists at least one column of type
Sequencewith numeric (float,double, orint) dtype. These columns can be interpreted as target time series. - For each row, all columns of type
Sequencehave same length. - Remaining columns of types other than
Sequence(e.g.,stringorfloat) can be interpreted as static covariates.
Datasets can be loaded using the 🤗 datasets library
import datasets
ds = datasets.load_dataset("autogluon/chronos_datasets", "m4_daily", split="train")
ds.set_format("numpy") # sequences returned as numpy arrays
NOTE: The
trainsplit of all datasets contains the full time series and has no relation to the train/test split used in the Chronos paper.
Example entry in the m4_daily dataset
>>> ds[0]
{'id': 'T000000',
'timestamp': array(['1994-03-01T12:00:00.000', '1994-03-02T12:00:00.000',
'1994-03-03T12:00:00.000', ..., '1996-12-12T12:00:00.000',
'1996-12-13T12:00:00.000', '1996-12-14T12:00:00.000'],
dtype='datetime64[ms]'),
'target': array([1017.1, 1019.3, 1017. , ..., 2071.4, 2083.8, 2080.6], dtype=float32),
'category': 'Macro'}
Converting to pandas
We can easily convert data in such format to a long format data frame
def to_pandas(ds: datasets.Dataset) -> "pd.DataFrame":
"""Convert dataset to long data frame format."""
sequence_columns = [col for col in ds.features if isinstance(ds.features[col], datasets.Sequence)]
return ds.to_pandas().explode(sequence_columns).infer_objects()
Example output
>>> print(to_pandas(ds).head())
id timestamp target category
0 T000000 1994-03-01 12:00:00 1017.1 Macro
1 T000000 1994-03-02 12:00:00 1019.3 Macro
2 T000000 1994-03-03 12:00:00 1017.0 Macro
3 T000000 1994-03-04 12:00:00 1019.2 Macro
4 T000000 1994-03-05 12:00:00 1018.7 Macro
Dealing with large datasets
Note that some datasets, such as subsets of WeatherBench, are extremely large (~100GB). To work with them efficiently, we recommend either loading them from disk (files will be downloaded to disk, but won't be all loaded into memory)
ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_daily", keep_in_memory=False, split="train")
or, for the largest datasets like weatherbench_hourly_temperature, reading them in streaming format (chunks will be downloaded one at a time)
ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_hourly_temperature", streaming=True, split="train")
License
Different datasets available in this collection are distributed under different open source licenses. Please see ds.info.license and ds.info.homepage for each individual dataset.
Citation
If you find these datasets useful for your research, please consider citing the associated paper:
@article{ansari2024chronos,
author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
title = {Chronos: Learning the Language of Time Series},
journal = {arXiv preprint arXiv:2403.07815},
year = {2024}
}