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--- |
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annotations_creators: |
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- no-annotation |
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license: other |
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source_datasets: |
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- original |
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task_categories: |
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- time-series-forecasting |
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task_ids: |
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- univariate-time-series-forecasting |
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- multivariate-time-series-forecasting |
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dataset_info: |
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- config_name: ETT_15T |
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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: HUFL |
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sequence: float32 |
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- name: HULL |
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sequence: float32 |
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- name: MUFL |
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sequence: float32 |
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- name: MULL |
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sequence: float32 |
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- name: LUFL |
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sequence: float32 |
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- name: LULL |
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sequence: float32 |
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- name: OT |
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sequence: float32 |
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splits: |
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- name: train |
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num_bytes: 5017042 |
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num_examples: 2 |
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download_size: 1964373 |
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dataset_size: 5017042 |
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- config_name: ETT_1H |
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features: |
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- name: id |
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dtype: string |
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sequence: float32 |
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sequence: float32 |
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sequence: float32 |
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sequence: float32 |
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sequence: float32 |
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- name: LULL |
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sequence: float32 |
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- name: OT |
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sequence: float32 |
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splits: |
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- name: train |
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num_bytes: 1254322 |
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num_examples: 2 |
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download_size: 531145 |
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dataset_size: 1254322 |
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- config_name: ETTh |
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features: |
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- name: id |
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dtype: string |
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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: 2229842 |
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num_examples: 2 |
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download_size: 569100 |
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dataset_size: 2229842 |
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- config_name: LOOP_SEATTLE_1D |
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features: |
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- name: target |
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sequence: float32 |
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sequence: timestamp[ms] |
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splits: |
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- name: train |
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num_bytes: 1419475 |
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num_examples: 323 |
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download_size: 750221 |
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dataset_size: 1419475 |
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- config_name: LOOP_SEATTLE_1H |
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features: |
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- name: target |
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sequence: float32 |
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- name: id |
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sequence: timestamp[ms] |
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splits: |
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- name: train |
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num_bytes: 33958495 |
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num_examples: 323 |
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download_size: 16373920 |
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dataset_size: 33958495 |
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- config_name: LOOP_SEATTLE_5T |
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features: |
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- name: target |
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sequence: float32 |
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sequence: timestamp[ms] |
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splits: |
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- name: train |
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num_bytes: 407449855 |
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num_examples: 323 |
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download_size: 209147833 |
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dataset_size: 407449855 |
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- config_name: M_DENSE_1D |
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features: |
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- name: target |
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sequence: float32 |
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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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splits: |
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- name: train |
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num_bytes: 263210 |
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num_examples: 30 |
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download_size: 132084 |
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dataset_size: 263210 |
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- config_name: M_DENSE_1H |
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features: |
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- name: target |
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sequence: float32 |
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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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splits: |
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- name: train |
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num_bytes: 6307610 |
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num_examples: 30 |
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download_size: 2055774 |
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dataset_size: 6307610 |
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- config_name: SZ_TAXI_15T |
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features: |
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- name: target |
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sequence: float32 |
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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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splits: |
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- name: train |
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num_bytes: 5573777 |
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num_examples: 156 |
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download_size: 2632475 |
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dataset_size: 5573777 |
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configs: |
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- config_name: ETT_15T |
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data_files: |
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- split: train |
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path: ETT/15T/train-* |
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- config_name: ETT_1H |
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data_files: |
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- split: train |
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path: ETT/1H/train-* |
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- config_name: ETTh |
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data_files: |
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- split: train |
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path: ETTh/train-* |
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- config_name: LOOP_SEATTLE_1D |
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data_files: |
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- split: train |
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path: LOOP_SEATTLE/1D/train-* |
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- config_name: LOOP_SEATTLE_1H |
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data_files: |
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- split: train |
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path: LOOP_SEATTLE/1H/train-* |
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- config_name: LOOP_SEATTLE_5T |
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data_files: |
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- split: train |
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path: LOOP_SEATTLE/5T/train-* |
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- config_name: M_DENSE_1D |
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data_files: |
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- split: train |
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path: M_DENSE/1D/train-* |
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- config_name: M_DENSE_1H |
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data_files: |
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- split: train |
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path: M_DENSE/1H/train-* |
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- config_name: SZ_TAXI_15T |
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data_files: |
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- split: train |
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path: SZ_TAXI/15T/train-* |
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--- |
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## Forecast evaluation datasets |
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This repository contains time series datasets that can be used for evaluation of univariate & multivariate forecasting models. |
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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. |
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The datasets follow a format that is compatible with the [`fev`](https://github.com/autogluon/fev) package. |
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## Data format and usage |
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Each dataset satisfies the following schema: |
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- each dataset entry (=row) represents a single univariate or multivariate time series |
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- 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) |
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