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: 42290010
dataset_size: 477140250
- 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:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ns]
- name: target
sequence: float32
splits:
- name: train
num_examples: 8
download_size: 14504261
- config_name: exchange_rate
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float32
splits:
- name: train
num_examples: 8
download_size: 401501
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:
- name: id
dtype: string
- 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
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: int64
splits:
- name: train
num_examples: 767
download_size: 117038
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_kdd_cup_2018
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: city
dtype: string
- name: station
dtype: string
- name: measurement
dtype: string
splits:
- name: train
num_bytes: 47091540
num_examples: 270
download_size: 8780105
dataset_size: 47091540
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_london_smart_meters
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 2664567976
num_examples: 5560
download_size: 597389119
dataset_size: 2664567976
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m1_monthly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 907691
num_examples: 617
download_size: 244372
dataset_size: 907691
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m1_quarterly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 162961
num_examples: 203
download_size: 48439
dataset_size: 162961
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m1_yearly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 75679
num_examples: 181
download_size: 30754
dataset_size: 75679
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m3_monthly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 2708124
num_examples: 1428
download_size: 589699
dataset_size: 2708124
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m3_quarterly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 606428
num_examples: 756
download_size: 188543
dataset_size: 606428
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_m3_yearly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 305359
num_examples: 645
download_size: 100184
dataset_size: 305359
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_nn5_weekly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float32
splits:
- name: train
num_examples: 111
download_size: 64620
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_pedestrian_counts
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: int64
splits:
- name: train
num_bytes: 50118790
num_examples: 66
download_size: 12377357
dataset_size: 50118790
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_rideshare
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: source_location
dtype: string
- name: provider_name
dtype: string
- name: provider_service
dtype: string
- name: price_min
sequence: float64
- name: price_mean
sequence: float64
- name: price_max
sequence: float64
- name: distance_min
sequence: float64
- name: distance_mean
sequence: float64
- name: distance_max
sequence: float64
- name: surge_min
sequence: float64
- name: surge_mean
sequence: float64
- name: surge_max
sequence: float64
- name: api_calls
sequence: float64
- name: temp
sequence: float64
- name: rain
sequence: float64
- name: humidity
sequence: float64
- name: clouds
sequence: float64
- name: wind
sequence: float64
splits:
- name: train
num_bytes: 10819910
num_examples: 156
download_size: 781873
dataset_size: 10819910
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_saugeenday
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: T1
sequence: float64
splits:
- name: train
num_bytes: 379875
num_examples: 1
download_size: 222678
dataset_size: 379875
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_temperature_rain
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: t_mean
sequence: float64
- name: prcp_sum
sequence: float64
- name: t_max
sequence: float64
- name: t_min
sequence: float64
- name: fcst_0_dailypop
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- name: fcst_0_dailypop1
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- name: fcst_0_dailypop10
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- name: fcst_0_dailypop15
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- name: fcst_0_dailypop25
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- name: fcst_0_dailypop5
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- name: fcst_0_dailypop50
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- name: fcst_0_dailyprecip
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- name: fcst_0_dailyprecip10pct
sequence: float64
- name: fcst_0_dailyprecip25pct
sequence: float64
- name: fcst_0_dailyprecip50pct
sequence: float64
- name: fcst_0_dailyprecip75pct
sequence: float64
- name: fcst_1_dailypop
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- name: fcst_1_dailypop1
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- name: fcst_1_dailypop10
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- name: fcst_1_dailypop15
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- name: fcst_1_dailypop25
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- name: fcst_1_dailypop50
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- name: fcst_1_dailyprecip
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- name: fcst_1_dailyprecip10pct
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- name: fcst_1_dailyprecip25pct
sequence: float64
- name: fcst_1_dailyprecip50pct
sequence: float64
- name: fcst_1_dailyprecip75pct
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- name: fcst_2_dailypop
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- name: fcst_2_dailypop1
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- name: fcst_2_dailypop15
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- name: fcst_2_dailypop25
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- name: fcst_2_dailypop5
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- name: fcst_2_dailypop50
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- name: fcst_2_dailyprecip
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- name: fcst_2_dailyprecip10pct
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- name: fcst_2_dailyprecip25pct
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- name: fcst_2_dailyprecip50pct
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- name: fcst_2_dailyprecip75pct
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- name: fcst_3_dailypop
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- name: fcst_3_dailyprecip10pct
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- name: fcst_5_dailypop1
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- name: fcst_5_dailypop25
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- name: fcst_5_dailypop50
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- name: fcst_5_dailyprecip10pct
sequence: float64
- name: fcst_5_dailyprecip25pct
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- name: fcst_5_dailyprecip50pct
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- name: fcst_5_dailyprecip75pct
sequence: float64
splits:
- name: train
num_bytes: 188598927
num_examples: 422
download_size: 44967856
dataset_size: 188598927
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_tourism_monthly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 1755434
num_examples: 366
download_size: 334951
dataset_size: 1755434
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_tourism_quarterly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 688817
num_examples: 427
download_size: 177407
dataset_size: 688817
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_tourism_yearly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
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num_examples: 518
download_size: 81479
dataset_size: 213954
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_traffic
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 241983226
num_examples: 862
download_size: 52748547
dataset_size: 241983226
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: monash_weather
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: subset
dtype: string
splits:
- name: train
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num_examples: 3010
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dataset_size: 688598539
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: nn5
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float32
splits:
- name: train
num_examples: 111
download_size: 203096
homepage: >-
http://www.neural-forecasting-competition.com/downloads/NN5/datasets/download.htm
- config_name: solar
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: power_mw
sequence: float64
- name: latitude
dtype: float64
- name: longitude
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- name: capacity_mw
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- name: subset
dtype: string
splits:
- name: train
num_bytes: 8689093932
num_examples: 5166
download_size: 1507924920
dataset_size: 8689093932
homepage: https://www.nrel.gov/grid/solar-power-data.html
- config_name: solar_1h
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: power_mw
sequence: float64
- name: latitude
dtype: float64
- name: longitude
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- name: capacity_mw
dtype: float64
- name: subset
dtype: string
splits:
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num_bytes: 724361772
num_examples: 5166
download_size: 124515417
dataset_size: 724361772
homepage: https://www.nrel.gov/grid/solar-power-data.html
- config_name: taxi_1h
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: subset
dtype: string
- name: lat
dtype: float64
- name: lng
dtype: float64
splits:
- name: train
num_bytes: 28832500
num_examples: 2428
download_size: 2265297
dataset_size: 28832500
license: Apache 2.0
homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets
- config_name: taxi_30min
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: subset
dtype: string
- name: lat
dtype: float64
- name: lng
dtype: float64
splits:
- name: train
num_bytes: 57560596
num_examples: 2428
download_size: 4541244
dataset_size: 57560596
license: Apache 2.0
homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets
- config_name: training_corpus_kernel_synth_1m
features:
- name: target
sequence: float64
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
splits:
- name: train
num_examples: 1000000
download_size: 8313239368
- config_name: training_corpus_tsmixup_10m
features:
- name: target
sequence: float64
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
splits:
- name: train
num_examples: 10000000
download_size: 82189589906
- config_name: uber_tlc_daily
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: int64
splits:
- name: train
num_examples: 262
download_size: 84747
homepage: https://github.com/fivethirtyeight/uber-tlc-foil-response
- config_name: uber_tlc_hourly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: int64
splits:
- name: train
num_examples: 262
download_size: 1878515
homepage: https://github.com/fivethirtyeight/uber-tlc-foil-response
- config_name: ushcn_daily
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: state
dtype: string
- name: coop_id
dtype: int64
- name: PRCP
sequence: float64
- name: SNOW
sequence: float64
- name: SNWD
sequence: float64
- name: TMAX
sequence: float64
- name: TMIN
sequence: float64
splits:
- name: train
num_bytes: 2259905202
num_examples: 1218
download_size: 221089890
dataset_size: 2259905202
homepage: https://data.ess-dive.lbl.gov/portals/CDIAC
- config_name: weatherbench_daily
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float32
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: level
dtype: float64
- name: subset
dtype: string
splits:
- name: train
num_bytes: 39510157312
num_examples: 225280
download_size: 18924392742
dataset_size: 39510157312
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_10m_u_component_of_wind
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: float64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 2048
download_size: 7292845757
dataset_size: 8617472000
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_10m_v_component_of_wind
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: float64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 2048
download_size: 7292352508
dataset_size: 8617472000
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_2m_temperature
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: float64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 2048
download_size: 7276396852
dataset_size: 8617453568
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_geopotential
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 87305564613
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_potential_vorticity
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 92426240043
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_relative_humidity
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 94728788382
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_specific_humidity
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 85139896451
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_temperature
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 94081539079
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_toa_incident_solar_radiation
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: float64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 2048
download_size: 6057953007
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_total_cloud_cover
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: float64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 2048
download_size: 6628258398
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_total_precipitation
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: float64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 2048
download_size: 6473160755
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_u_component_of_wind
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 94801498563
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_v_component_of_wind
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 94800557482
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_hourly_vorticity
features:
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: target
sequence: float32
- name: level
dtype: int64
- name: timestamp
sequence: timestamp[ms]
- name: subset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 26624
download_size: 94720960560
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: weatherbench_weekly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float32
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: level
dtype: float64
- name: subset
dtype: string
splits:
- name: train
num_bytes: 5656029184
num_examples: 225280
download_size: 2243012083
dataset_size: 5656029184
license: MIT
homepage: https://github.com/pangeo-data/WeatherBench
- config_name: wiki_daily_100k
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
- name: page_name
dtype: string
splits:
- name: train
num_bytes: 4389782678
num_examples: 100000
download_size: 592554033
dataset_size: 4389782678
license: CC0
homepage: https://dumps.wikimedia.org/other/pageviews/readme.html
- config_name: wind_farms_daily
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 1919187
num_examples: 337
download_size: 598834
dataset_size: 1919187
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
- config_name: wind_farms_hourly
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[ms]
- name: target
sequence: float64
splits:
- name: train
num_bytes: 45917027
num_examples: 337
download_size: 12333116
dataset_size: 45917027
license: CC BY 4.0
homepage: https://zenodo.org/communities/forecasting
configs:
- config_name: dominick
data_files:
- split: train
path: dominick/train-*
- config_name: electricity_15min
data_files:
- split: train
path: electricity_15min/train-*
- config_name: ercot
data_files:
- split: train
path: ercot/train-*
- config_name: exchange_rate
data_files:
- split: train
path: exchange_rate/train-*
- config_name: m4_daily
data_files:
- split: train
path: m4_daily/train-*
- config_name: m4_hourly
data_files:
- split: train
path: m4_hourly/train-*
- config_name: m4_monthly
data_files:
- split: train
path: m4_monthly/train-*
- config_name: m4_quarterly
data_files:
- split: train
path: m4_quarterly/train-*
- config_name: m4_weekly
data_files:
- split: train
path: m4_weekly/train-*
- config_name: m4_yearly
data_files:
- split: train
path: m4_yearly/train-*
- config_name: m5
data_files:
- split: train
path: m5/train-*
- config_name: mexico_city_bikes
data_files:
- split: train
path: mexico_city_bikes/train-*
- config_name: monash_australian_electricity
data_files:
- 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: training_corpus_kernel_synth_1m
data_files:
- split: train
path: training_corpus/kernel_synth_1m/train-*
- config_name: training_corpus_tsmixup_10m
data_files:
- split: train
path: training_corpus/tsmixup_10m/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")
Chronos training corpus with TSMixup & KernelSynth
The training corpus used for training the Chronos models can be loaded via the configs training_corpus_tsmixup_10m (10M TSMixup augmentations of real-world data) and training_corpus_kernel_synth_1m (1M synthetic time series generated with KernelSynth), e.g.,
ds = datasets.load_dataset("autogluon/chronos_datasets", "training_corpus_tsmixup_10m", streaming=True, split="train")
Note that since data in the training corpus was obtained by combining various synthetic & real-world time series, the timestamps contain dummy values that have no connection to the original data.
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}
}