Duplicate from autogluon/chronos_datasets

Co-authored-by: Oleksandr Shchur <[email protected]>

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README.md

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+---
+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:
+  - 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
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+    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
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+    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:
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+    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:
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+    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:
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+    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
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+  - name: price_mean
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+  - name: distance_min
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+  - name: distance_mean
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+  - name: surge_min
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+  - name: surge_mean
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+  - 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
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+    sequence: float64
+  - name: fcst_4_dailypop50
+    sequence: float64
+  - name: fcst_4_dailyprecip
+    sequence: float64
+  - name: fcst_4_dailyprecip10pct
+    sequence: float64
+  - name: fcst_4_dailyprecip25pct
+    sequence: float64
+  - name: fcst_4_dailyprecip50pct
+    sequence: float64
+  - name: fcst_4_dailyprecip75pct
+    sequence: float64
+  - name: fcst_5_dailypop
+    sequence: float64
+  - name: fcst_5_dailypop1
+    sequence: float64
+  - name: fcst_5_dailypop10
+    sequence: float64
+  - name: fcst_5_dailypop15
+    sequence: float64
+  - name: fcst_5_dailypop25
+    sequence: float64
+  - name: fcst_5_dailypop5
+    sequence: float64
+  - name: fcst_5_dailypop50
+    sequence: float64
+  - name: fcst_5_dailyprecip
+    sequence: float64
+  - name: fcst_5_dailyprecip10pct
+    sequence: float64
+  - name: fcst_5_dailyprecip25pct
+    sequence: float64
+  - name: fcst_5_dailyprecip50pct
+    sequence: float64
+  - 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
+    num_bytes: 213954
+    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
+    num_bytes: 688598539
+    num_examples: 3010
+  download_size: 133164027
+  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
+    dtype: float64
+  - name: capacity_mw
+    dtype: float64
+  - 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
+    dtype: float64
+  - name: capacity_mw
+    dtype: float64
+  - name: subset
+    dtype: string
+  splits:
+  - name: train
+    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
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+  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
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+  - name: id
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+  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
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+  - name: level
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+    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](https://github.com/amazon-science/chronos-forecasting) 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`](https://huggingface.co/datasets/autogluon/chronos_datasets_extra).
+
+See the [paper](https://arxiv.org/abs/2403.07815) 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 `id` and type `string` that contains the unique identifier of each time series.
+- There exists one column of type `Sequence` with dtype `timestamp[ms]`. This column contains the timestamps of the observations. Timestamps are guaranteed to have a regular frequency that can be obtained with [`pandas.infer_freq`](https://pandas.pydata.org/docs/reference/api/pandas.infer_freq.html).
+- There exists at least one column of type `Sequence` with numeric (`float`, `double`, or `int`) dtype. These columns can be interpreted as target time series.
+- For each row, all columns of type `Sequence` have same length.
+- Remaining columns of types other than `Sequence` (e.g., `string` or `float`) can be interpreted as static covariates.
+
+Datasets can be loaded using the 🤗 [`datasets`](https://huggingface.co/docs/datasets/en/index) library
+```python
+import datasets
+
+ds = datasets.load_dataset("autogluon/chronos_datasets", "m4_daily", split="train")
+ds.set_format("numpy")  # sequences returned as numpy arrays
+```
+
+> **NOTE:**  The `train` split 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
+```python
+>>> 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
+```python
+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
+```python
+>>> 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)
+```python
+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)
+```python
+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.,
+```python
+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:
+```markdown
+@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}
+}
+```

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