|
|
--- |
|
|
annotations_creators: |
|
|
- no-annotation |
|
|
license: other |
|
|
source_datasets: |
|
|
- original |
|
|
task_categories: |
|
|
- time-series-forecasting |
|
|
task_ids: |
|
|
- univariate-time-series-forecasting |
|
|
- multivariate-time-series-forecasting |
|
|
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|
|
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|
|
- config_name: epf_electricity_be |
|
|
features: |
|
|
- name: id |
|
|
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|
|
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|
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|
|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
- config_name: epf_electricity_de |
|
|
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|
|
- name: id |
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
sequence: float64 |
|
|
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|
|
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|
|
splits: |
|
|
- name: train |
|
|
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|
|
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|
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|
|
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|
|
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|
|
features: |
|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
download_size: 902996 |
|
|
dataset_size: 1677334 |
|
|
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|
|
features: |
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
- config_name: favorita_store_sales |
|
|
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|
|
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|
|
dtype: string |
|
|
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|
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|
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|
|
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|
|
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|
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|
|
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|
download_size: 8385672 |
|
|
dataset_size: 113609820 |
|
|
- config_name: favorita_transactions |
|
|
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|
|
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|
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dtype: int64 |
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sequence: timestamp[us] |
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|
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splits: |
|
|
- name: train |
|
|
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|
|
num_examples: 54 |
|
|
download_size: 207866 |
|
|
dataset_size: 2711975 |
|
|
- config_name: m5_with_covariates |
|
|
features: |
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|
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dtype: string |
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|
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|
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|
|
download_size: 81672751 |
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dataset_size: 3815531330 |
|
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- config_name: proenfo_bull |
|
|
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|
|
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|
dtype: string |
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|
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sequence: timestamp[ms] |
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sequence: float64 |
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sequence: float64 |
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splits: |
|
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- name: train |
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num_examples: 41 |
|
|
download_size: 3893651 |
|
|
dataset_size: 28773967 |
|
|
- config_name: proenfo_cockatoo |
|
|
features: |
|
|
- name: id |
|
|
dtype: string |
|
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sequence: timestamp[ms] |
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sequence: float64 |
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sequence: float64 |
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sequence: float64 |
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sequence: float64 |
|
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splits: |
|
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- name: train |
|
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num_bytes: 982517 |
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|
|
download_size: 408973 |
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dataset_size: 982517 |
|
|
- config_name: proenfo_covid19 |
|
|
features: |
|
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- name: id |
|
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dtype: string |
|
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sequence: timestamp[ms] |
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sequence: float64 |
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- name: humidity_perc |
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sequence: float64 |
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sequence: float64 |
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- name: wind_direction_deg |
|
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sequence: float64 |
|
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- name: wind_speed_kmh |
|
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sequence: float64 |
|
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|
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- name: train |
|
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num_bytes: 2042408 |
|
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num_examples: 1 |
|
|
download_size: 965912 |
|
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dataset_size: 2042408 |
|
|
- config_name: proenfo_gfc12_load |
|
|
features: |
|
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- name: id |
|
|
dtype: string |
|
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- name: timestamp |
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sequence: timestamp[ms] |
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sequence: float64 |
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- name: airtemperature |
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sequence: float64 |
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splits: |
|
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- name: train |
|
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|
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num_examples: 11 |
|
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download_size: 3161406 |
|
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dataset_size: 10405494 |
|
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- config_name: proenfo_gfc14_load |
|
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features: |
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- name: id |
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dtype: string |
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- name: timestamp |
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sequence: timestamp[ms] |
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- name: target |
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sequence: float64 |
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- name: airtemperature |
|
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sequence: float64 |
|
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splits: |
|
|
- name: train |
|
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num_bytes: 420500 |
|
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num_examples: 1 |
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download_size: 200463 |
|
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dataset_size: 420500 |
|
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- config_name: proenfo_gfc17_load |
|
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|
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- name: id |
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dtype: string |
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sequence: timestamp[ms] |
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sequence: float64 |
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- name: airtemperature |
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sequence: int64 |
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splits: |
|
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- name: train |
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num_bytes: 3368608 |
|
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num_examples: 8 |
|
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download_size: 1562067 |
|
|
dataset_size: 3368608 |
|
|
- config_name: proenfo_hog |
|
|
features: |
|
|
- name: id |
|
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dtype: string |
|
|
- name: timestamp |
|
|
sequence: timestamp[ms] |
|
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- name: target |
|
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sequence: float64 |
|
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- name: airtemperature |
|
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sequence: float64 |
|
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- name: dewtemperature |
|
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sequence: float64 |
|
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- name: sealvlpressure |
|
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sequence: float64 |
|
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- name: winddirection |
|
|
sequence: float64 |
|
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- name: windspeed |
|
|
sequence: float64 |
|
|
splits: |
|
|
- name: train |
|
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|
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num_examples: 24 |
|
|
download_size: 3291179 |
|
|
dataset_size: 23580325 |
|
|
- config_name: proenfo_pdb |
|
|
features: |
|
|
- name: id |
|
|
dtype: string |
|
|
- name: timestamp |
|
|
sequence: timestamp[ms] |
|
|
- name: target |
|
|
sequence: float64 |
|
|
- name: airtemperature |
|
|
sequence: int64 |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 420500 |
|
|
num_examples: 1 |
|
|
download_size: 226285 |
|
|
dataset_size: 420500 |
|
|
- config_name: proenfo_spain |
|
|
features: |
|
|
- name: id |
|
|
dtype: string |
|
|
- name: timestamp |
|
|
sequence: timestamp[ms] |
|
|
- name: target |
|
|
sequence: float64 |
|
|
- name: generation_biomass |
|
|
sequence: float64 |
|
|
- name: generation_fossil_brown_coal_lignite |
|
|
sequence: float64 |
|
|
- name: generation_fossil_coal_derived_gas |
|
|
sequence: float64 |
|
|
- name: generation_fossil_gas |
|
|
sequence: float64 |
|
|
- name: generation_fossil_hard_coal |
|
|
sequence: float64 |
|
|
- name: generation_fossil_oil |
|
|
sequence: float64 |
|
|
- name: generation_fossil_oil_shale |
|
|
sequence: float64 |
|
|
- name: generation_fossil_peat |
|
|
sequence: float64 |
|
|
- name: generation_geothermal |
|
|
sequence: float64 |
|
|
- name: generation_hydro_pumped_storage_consumption |
|
|
sequence: float64 |
|
|
- name: generation_hydro_run_of_river_and_poundage |
|
|
sequence: float64 |
|
|
- name: generation_hydro_water_reservoir |
|
|
sequence: float64 |
|
|
- name: generation_marine |
|
|
sequence: float64 |
|
|
- name: generation_nuclear |
|
|
sequence: float64 |
|
|
- name: generation_other |
|
|
sequence: float64 |
|
|
- name: generation_other_renewable |
|
|
sequence: float64 |
|
|
- name: generation_solar |
|
|
sequence: float64 |
|
|
- name: generation_waste |
|
|
sequence: float64 |
|
|
- name: generation_wind_offshore |
|
|
sequence: float64 |
|
|
- name: generation_wind_onshore |
|
|
sequence: float64 |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 6171357 |
|
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num_examples: 1 |
|
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download_size: 1275626 |
|
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dataset_size: 6171357 |
|
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configs: |
|
|
- config_name: ETTh |
|
|
data_files: |
|
|
- split: train |
|
|
path: ETTh/train-* |
|
|
- config_name: ETTm |
|
|
data_files: |
|
|
- split: train |
|
|
path: ETTm/train-* |
|
|
- config_name: epf_electricity_be |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf/electricity_be/train-* |
|
|
- config_name: epf_electricity_de |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf/electricity_de/train-* |
|
|
- config_name: epf_electricity_fr |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf/electricity_fr/train-* |
|
|
- config_name: epf_electricity_np |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf/electricity_np/train-* |
|
|
- config_name: epf_electricity_pjm |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf/electricity_pjm/train-* |
|
|
- config_name: favorita_store_sales |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita/store_sales/train-* |
|
|
- config_name: favorita_transactions |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita/transactions/train-* |
|
|
- config_name: m5_with_covariates |
|
|
data_files: |
|
|
- split: train |
|
|
path: m5_with_covariates/train-* |
|
|
- config_name: proenfo_bull |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/bull/train-* |
|
|
- config_name: proenfo_cockatoo |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/cockatoo/train-* |
|
|
- config_name: proenfo_covid19 |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/covid19/train-* |
|
|
- config_name: proenfo_gfc12_load |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/gfc12_load/train-* |
|
|
- config_name: proenfo_gfc14_load |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/gfc14_load/train-* |
|
|
- config_name: proenfo_gfc17_load |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/gfc17_load/train-* |
|
|
- config_name: proenfo_hog |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/hog/train-* |
|
|
- config_name: proenfo_pdb |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/pdb/train-* |
|
|
- config_name: proenfo_spain |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo/spain/train-* |
|
|
--- |
|
|
|
|
|
## Forecast evaluation datasets |
|
|
|
|
|
This repository contains time series datasets that can be used for evaluation of univariate & multivariate forecasting models. |
|
|
|
|
|
The main focus of this repository is on datasets that reflect real-world forecasting scenarios, such as those involving covariates, missing values, and other practical complexities. |
|
|
|
|
|
The datasets follow a format that is compatible with the [`fev`](https://github.com/autogluon/fev) package. |
|
|
|
|
|
## Data format and usage |
|
|
|
|
|
Each dataset satisfies the following schema: |
|
|
- each dataset entry (=row) represents a single univariate or multivariate time series |
|
|
- each entry contains |
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- 1/ a field of type `Sequence(timestamp)` that contains the timestamps of observations |
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- 2/ at least one field of type `Sequence(float)` that can be used as the target time series or dynamic covariates |
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- 3/ a field of type `string` that contains the unique ID of each time series |
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- all fields of type `Sequence` have the same length |
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Datasets can be loaded using the [🤗 `datasets`](https://huggingface.co/docs/datasets/en/index) library. |
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```python |
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import datasets |
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ds = datasets.load_dataset("autogluon/fev_datasets", "epf_electricity_de", split="train") |
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ds.set_format("numpy") # sequences returned as numpy arrays |
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``` |
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Example entry in the `epf_electricity_de` dataset |
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```python |
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>>> ds[0] |
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{'id': 'DE', |
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'timestamp': array(['2012-01-09T00:00:00.000000', '2012-01-09T01:00:00.000000', |
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'2012-01-09T02:00:00.000000', ..., '2017-12-31T21:00:00.000000', |
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'2017-12-31T22:00:00.000000', '2017-12-31T23:00:00.000000'], |
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dtype='datetime64[us]'), |
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'target': array([34.97, 33.43, 32.74, ..., 5.3 , 1.86, -0.92], dtype=float32), |
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'Ampirion Load Forecast': array([16382. , 15410.5, 15595. , ..., 15715. , 15876. , 15130. ], |
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dtype=float32), |
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'PV+Wind Forecast': array([ 3569.5276, 3315.275 , 3107.3076, ..., 29653.008 , 29520.33 , |
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29466.408 ], dtype=float32)} |
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``` |
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For more details about the dataset format and usage, check out the [`fev` documentation on GitHub](https://github.com/autogluon/fev?tab=readme-ov-file#tutorials). |
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## Dataset statistics |
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**Disclaimer:** These datasets have been converted into a unified format from external sources. Please refer to the original sources for licensing and citation terms. We do not claim any rights to the original data. Unless otherwise specified, the datasets are provided only for research purposes. |
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| config | freq | # items | # obs | # dynamic cols | # static cols | source | citation | |
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|:------------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| |
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| `ETTh` | h | 2 | 243880 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) | |
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| `ETTm` | 15min | 2 | 975520 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) | |
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| `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | |
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| `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | |
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| `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | |
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| `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | |
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| `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | |
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| `favorita_store_sales` | D | 1782 | 12032064 | 4 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[3]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) | |
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| `favorita_transactions` | D | 54 | 273456 | 3 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[3]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) | |
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| `m5_with_covariates` | D | 30490 | 428849460 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [[4]](https://doi.org/10.1016/j.ijforecast.2021.07.007) | |
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| `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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| `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | |
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## Publications using these datasets |
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- ["ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"](https://arxiv.org/abs/2503.12107) |
