Duplicate from autogluon/chronos_datasets

Co-authored-by: Oleksandr Shchur <shchuro@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,55 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,1771 @@
+---
+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
+    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
+    sequence: float64
+  - name: fcst_0_dailypop1
+    sequence: float64
+  - name: fcst_0_dailypop10
+    sequence: float64
+  - name: fcst_0_dailypop15
+    sequence: float64
+  - name: fcst_0_dailypop25
+    sequence: float64
+  - name: fcst_0_dailypop5
+    sequence: float64
+  - name: fcst_0_dailypop50
+    sequence: float64
+  - name: fcst_0_dailyprecip
+    sequence: float64
+  - 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
+    sequence: float64
+  - name: fcst_1_dailypop1
+    sequence: float64
+  - name: fcst_1_dailypop10
+    sequence: float64
+  - name: fcst_1_dailypop15
+    sequence: float64
+  - name: fcst_1_dailypop25
+    sequence: float64
+  - name: fcst_1_dailypop5
+    sequence: float64
+  - name: fcst_1_dailypop50
+    sequence: float64
+  - name: fcst_1_dailyprecip
+    sequence: float64
+  - name: fcst_1_dailyprecip10pct
+    sequence: float64
+  - name: fcst_1_dailyprecip25pct
+    sequence: float64
+  - name: fcst_1_dailyprecip50pct
+    sequence: float64
+  - name: fcst_1_dailyprecip75pct
+    sequence: float64
+  - name: fcst_2_dailypop
+    sequence: float64
+  - name: fcst_2_dailypop1
+    sequence: float64
+  - name: fcst_2_dailypop10
+    sequence: float64
+  - name: fcst_2_dailypop15
+    sequence: float64
+  - name: fcst_2_dailypop25
+    sequence: float64
+  - name: fcst_2_dailypop5
+    sequence: float64
+  - name: fcst_2_dailypop50
+    sequence: float64
+  - name: fcst_2_dailyprecip
+    sequence: float64
+  - name: fcst_2_dailyprecip10pct
+    sequence: float64
+  - name: fcst_2_dailyprecip25pct
+    sequence: float64
+  - name: fcst_2_dailyprecip50pct
+    sequence: float64
+  - name: fcst_2_dailyprecip75pct
+    sequence: float64
+  - name: fcst_3_dailypop
+    sequence: float64
+  - name: fcst_3_dailypop1
+    sequence: float64
+  - name: fcst_3_dailypop10
+    sequence: float64
+  - name: fcst_3_dailypop15
+    sequence: float64
+  - name: fcst_3_dailypop25
+    sequence: float64
+  - name: fcst_3_dailypop5
+    sequence: float64
+  - name: fcst_3_dailypop50
+    sequence: float64
+  - name: fcst_3_dailyprecip
+    sequence: float64
+  - name: fcst_3_dailyprecip10pct
+    sequence: float64
+  - name: fcst_3_dailyprecip25pct
+    sequence: float64
+  - name: fcst_3_dailyprecip50pct
+    sequence: float64
+  - name: fcst_3_dailyprecip75pct
+    sequence: float64
+  - name: fcst_4_dailypop
+    sequence: float64
+  - name: fcst_4_dailypop1
+    sequence: float64
+  - name: fcst_4_dailypop10
+    sequence: float64
+  - name: fcst_4_dailypop15
+    sequence: float64
+  - name: fcst_4_dailypop25
+    sequence: float64
+  - name: fcst_4_dailypop5
+    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
+    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](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}
+}
+```

dominick/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:098ea1ba7c2343f2ffa696a7388beda1ac4e0446af57291a1a7c6f7442f8ba1f
+size 60199910

electricity_15min/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00016bc6a1abb9c759aa7c72bddc538d4934353afa9c0518ac9080850ff585f8
+size 284497403

ercot/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2484d08da8bac619546eca00ab86d987a559e02e413c33c512d3ca6552877a58
+size 14504261

exchange_rate/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c214a83fe8d21ef6aea8a230d9131601e5a0a0157b1b0c713470974f1e565edc
+size 401501

m4_daily/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:871f418ed99e8cc8433cc03f4a3e8e2c2387b31fa4fee8b36f6d062df38fb87b
+size 65546675

m4_hourly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ecf702e63a3ebfd5b0866cec8c2448fcc9fc5a52584480885a5142d34061d36f
+size 1336971

m4_monthly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15576f826405d4a35fe7d264b2a377ef2407d7f3370783d274542b29792c0ee8
+size 52772258

m4_quarterly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a73cd1347c4da84eedfe90ff44858a3a1198fbdcbfb8d7ab5c46c2d65cfde217
+size 13422579

m4_weekly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d4e7b9a0e920a932fc3e8da0a422058bfe6adcc8e9fedd28a343daf1a2f7b9e
+size 2556691

m4_yearly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd77637b0b5c651da97c7fb8033b21766348917099e14746457e2a004341c14f
+size 5488601

m5/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df93472c09b4cd7fcb5b5a2c863fc0827959d7015ba3a2c2b8f5fcf8bce3eff5
+size 78063286

mexico_city_bikes/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d3bed5fd869b1a08af9399f2c5b850d666a763c8c1038ff636c6fa47aefc788
+size 103206946

monash_australian_electricity/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba9cf43c70b00b2480199abae4a400d4b394229afb08217557d97dbb5ffd3c6a
+size 16856156

monash_car_parts/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d31a7d55f41a3d326888fc50da9f8b0ba99b5014a4ab18d375876aa109c31fe
+size 70278

monash_cif_2016/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c570a2f42b15adeba850d2d2f76b06469996fc81aa1ee92a1a1b3229ed00f6e
+size 70876

monash_covid_deaths/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74b6e0c79e1eff821b4bcbc4d1b0a53674e6f22bf70dbd4495c7aa49baf46421
+size 58957

monash_electricity_hourly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9a0bf2b13d6b4f93afbaa97b47db064a7ac7740f6c8e40f9a97261c475c9aee
+size 31139117

monash_electricity_weekly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75bcd063612a657cfe19178d767fe89770b63e9fb5c606d50e9b6b1b7dea92cc
+size 333563

monash_fred_md/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55d622cf7a51cccb1fd4bdedd58f17a8409252450a20889e36bfa7fd811cae2e
+size 412207

monash_hospital/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30835432b2abedd5ce22419cbd10cafa75d82053248d15232711c4cb1ec144a7
+size 117038

monash_kdd_cup_2018/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1dab9ce582d1fbb0a99dcc5e8f44104871a95484ff4dae605f705a5abfd78a8c
+size 8780105

monash_london_smart_meters/train-00000-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:461bafd74a385b1be117ad16f447d8df088ce9e90dee6bd125ce5e3d9541b39b
+size 204153500

monash_london_smart_meters/train-00001-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e051e67d697d781eba863d59a07779c2f63252245b856a8f2b85d80abc3668
+size 198328771

monash_london_smart_meters/train-00002-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25b7024b9f50c8188be77a069c51d8b7ec86e8745d856fc7f934e6cbac747109
+size 194906848

monash_m1_monthly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e55d14f1605e17b4841ecc64d5dfde507a6fb2a7596e6d246c0556fd6fa059be
+size 244372

monash_m1_quarterly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0eaa36bbe95e310ee2679171b0151a368afd6e2a2edad13a942dbe658f44d1f
+size 48439

monash_m1_yearly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7260590626a7b7395b3291a55d35bc399b12e6ed72ce93d83e324e78ef875097
+size 30754

monash_m3_monthly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:880053cdc255c04f959225eca23db3371d9c3163075857582b19cfd702293b6e
+size 589699

monash_m3_quarterly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:502ec27dca4d629e6eca0599f06915a2f4fd22dae2e374d4bd66fe437106b2a8
+size 188543

monash_m3_yearly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48b346b4922133bc6d0b85a39835308a85dd4f7108f4d9ad18dcc695fe8f3696
+size 100184

monash_nn5_weekly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4848302e4e5645187e425fcc09e4c7da0151f13d0c2fc65fd722f9956dd32c2
+size 64620

monash_pedestrian_counts/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6bdda69390f539cd77a2b6c4833c577b2678b85ae1c9bb98fd43203b86ebd8e1
+size 12377357

monash_rideshare/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25012bbf8d99a4dfde57f5c7d212f03a039b7c36f9d110b22d18871af5e10404
+size 781873

monash_saugeenday/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f56c40b85baf50902d26f0ecdb8037a1ca95841dc92bacd458cad7664bfcd42
+size 222678

monash_temperature_rain/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcbd2b694765dab947ba425e038b69e3183b272d480f42ff5d89899f01c0f094
+size 44967856

monash_tourism_monthly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac8d1c6a87cbdea71c1327828c58d9628f0be3dd322da545e17425b427c898ee
+size 334951

monash_tourism_quarterly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07069411c08f6781e15e6d42e93c6811e072e972003d42fe00a1e332b46d1051
+size 177407

monash_tourism_yearly/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a23bae41bc76d02db0b494cddd2adafa29c2d7ca260bbe0f62704419282b44a
+size 81479

monash_traffic/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2ef1656931b3a33925817c461ccb724dfd19e948ca4c237ead59fc5e6be768f
+size 52748547

monash_weather/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50f7ebecd006b9bff3f5b872924efd1e8820bcaf0ab9973d10138924dc762643
+size 133164027

nn5/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85051a7c1d000174e009fbb7ac9ecf076d375ae6ba598e2413f234091c763670
+size 203096

solar/train-00000-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26fa2494ac7b27ec3c2ed7af7cb6bac1b2cff360248a4b6b47f4e68c4083d474
+size 167981038

solar/train-00001-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea3e11b53b899b542ea2160924ff03666e5f895ff5d8655cf3266772e87bbb67
+size 169678766

solar/train-00002-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:561926b6b8208e5c0c9216674f0b2da8d2fa37af7d4e2beb5ba3d51abe2cd554
+size 165665816

solar/train-00003-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fadceb4fee8232cbbb6edfa7a9d5c4094ef0cb8aee49bead112f807dfb034861
+size 167599678

solar/train-00004-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5b969d738f928657d51c01f3cd153d12f0c414bed71d177eff81fd9624d2ae9
+size 167206006

solar/train-00005-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc7e87d7af9460b4a431b1c05f3d31d56f94cd615f99590795caea4c03912665
+size 166650129

solar/train-00006-of-00009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbf71b7760b4b677b8932fa22cc8c1d15f1d4ef2edeefd04aecabf0e899a014e
+size 166697112