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--- |
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license: openrail |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- time-series-forecasting |
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dataset_info: |
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features: |
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- name: start |
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dtype: timestamp[us] |
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- name: target |
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sequence: float64 |
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- name: feat_static_cat |
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sequence: int64 |
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- name: feat_dynamic_real |
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dtype: 'null' |
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- name: item_id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 77588260 |
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num_examples: 370 |
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- name: validation |
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num_bytes: 77730340 |
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num_examples: 370 |
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download_size: 82303497 |
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dataset_size: 155318600 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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--- |
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**Download the Dataset**: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("LeoTungAnh/electricity_hourly") |
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``` |
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**Dataset Card for Electricity Consumption** |
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This dataset encompasses hourly electricity consumption in kilowatts (kW) across a span of three years (2012-2014), involving 370 individual clients in Portugal. |
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**Preprocessing information**: |
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- Grouped by hour (frequency: "1H"). |
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- Applied Standardization as preprocessing technique ("Std"). |
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**Dataset information**: |
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- Number of time series: 370 |
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- Number of training samples: 26208 |
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- Number of validation samples: 26256 (number_of_training_samples + 48) |
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- Number of testing samples: 26304 (number_of_validation_samples + 48) |
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**Dataset format**: |
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```python |
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Dataset({ |
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features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'], |
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num_rows: 370 |
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}) |
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``` |
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**Data format for a sample**: |
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- 'start': datetime.datetime |
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- 'target': list of a time series data |
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- 'feat_static_cat': time series index |
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- 'feat_dynamic_real': None |
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- 'item_id': name of time series |
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**Data example**: |
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```python |
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{'start': datetime.datetime(2012, 1, 1, 1, 0), |
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'target': [-0.19363673541224083, -0.08851588245610625, -0.19363673541224083, ... -0.5615597207587115,...], |
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'feat_static_cat': [0], |
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'feat_dynamic_real': None, |
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'item_id': 'MT_001' |
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} |
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``` |
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**Usage**: |
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- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface. |
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- Other algorithms can extract data directly by making use of 'target' feature. |