--- annotations_creators: - no-annotation license: other source_datasets: - original task_categories: - time-series-forecasting task_ids: - univariate-time-series-forecasting - multivariate-time-series-forecasting pretty_name: Chronos datasets dataset_info: - config_name: dominick features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: im_0 dtype: int64 splits: - name: train num_bytes: 477140250 num_examples: 100014 download_size: 42290010 dataset_size: 477140250 - config_name: electricity_15min features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: consumption_kW sequence: float64 splits: - name: train num_bytes: 670989988 num_examples: 370 download_size: 284497403 dataset_size: 670989988 license: CC BY 4.0 homepage: https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014 - config_name: ercot features: - name: id dtype: string - name: timestamp sequence: timestamp[ns] - name: target sequence: float32 splits: - name: train num_examples: 8 download_size: 14504261 - config_name: exchange_rate features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 8 download_size: 401501 license: MIT homepage: https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate - config_name: m4_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 160504176 num_examples: 4227 download_size: 65546675 dataset_size: 160504176 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 5985544 num_examples: 414 download_size: 1336971 dataset_size: 5985544 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 181372969 num_examples: 48000 download_size: 52772258 dataset_size: 181372969 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 39205397 num_examples: 24000 download_size: 13422579 dataset_size: 39205397 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 5955806 num_examples: 359 download_size: 2556691 dataset_size: 5955806 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 14410042 num_examples: 23000 download_size: 5488601 dataset_size: 14410042 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m5 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: item_id dtype: string - name: target sequence: float32 - name: dept_id dtype: string - name: cat_id dtype: string - name: store_id dtype: string - name: state_id dtype: string splits: - name: train num_bytes: 574062630 num_examples: 30490 download_size: 78063286 dataset_size: 574062630 homepage: https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules - config_name: mexico_city_bikes features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 618999406 num_examples: 494 download_size: 103206946 dataset_size: 618999406 homepage: https://ecobici.cdmx.gob.mx/en/open-data/ - config_name: monash_australian_electricity features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 18484319 num_examples: 5 download_size: 16856156 dataset_size: 18484319 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_car_parts features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2232790 num_examples: 2674 download_size: 70278 dataset_size: 2232790 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_cif_2016 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 115096 num_examples: 72 download_size: 70876 dataset_size: 115096 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_covid_deaths features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 907326 num_examples: 266 download_size: 58957 dataset_size: 907326 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_electricity_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 135103443 num_examples: 321 download_size: 31139117 dataset_size: 135103443 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_electricity_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 807315 num_examples: 321 download_size: 333563 dataset_size: 807315 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_fred_md features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1248369 num_examples: 107 download_size: 412207 dataset_size: 1248369 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_hospital features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 767 download_size: 117038 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_kdd_cup_2018 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: city dtype: string - name: station dtype: string - name: measurement dtype: string splits: - name: train num_bytes: 47091540 num_examples: 270 download_size: 8780105 dataset_size: 47091540 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_london_smart_meters features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2664567976 num_examples: 5560 download_size: 597389119 dataset_size: 2664567976 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 907691 num_examples: 617 download_size: 244372 dataset_size: 907691 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 162961 num_examples: 203 download_size: 48439 dataset_size: 162961 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 75679 num_examples: 181 download_size: 30754 dataset_size: 75679 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2708124 num_examples: 1428 download_size: 589699 dataset_size: 2708124 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 606428 num_examples: 756 download_size: 188543 dataset_size: 606428 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 305359 num_examples: 645 download_size: 100184 dataset_size: 305359 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_nn5_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 111 download_size: 64620 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_pedestrian_counts features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_bytes: 50118790 num_examples: 66 download_size: 12377357 dataset_size: 50118790 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_rideshare features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: source_location dtype: string - name: provider_name dtype: string - name: provider_service dtype: string - name: price_min sequence: float64 - name: price_mean sequence: float64 - name: price_max sequence: float64 - name: distance_min sequence: float64 - name: distance_mean sequence: float64 - name: distance_max sequence: float64 - name: surge_min sequence: float64 - name: surge_mean sequence: float64 - name: surge_max sequence: float64 - name: api_calls sequence: float64 - name: temp sequence: float64 - name: rain sequence: float64 - name: humidity sequence: float64 - name: clouds sequence: float64 - name: wind sequence: float64 splits: - name: train num_bytes: 10819910 num_examples: 156 download_size: 781873 dataset_size: 10819910 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_saugeenday features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: T1 sequence: float64 splits: - name: train num_bytes: 379875 num_examples: 1 download_size: 222678 dataset_size: 379875 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_temperature_rain features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: t_mean sequence: float64 - name: prcp_sum sequence: float64 - name: t_max sequence: float64 - name: t_min sequence: float64 - name: fcst_0_dailypop 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} } ```