Datasets:
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---
license: openrail
size_categories:
- 1K<n<10K
task_categories:
- time-series-forecasting
dataset_info:
features:
- name: start
dtype: timestamp[us]
- name: target
sequence: float64
- name: feat_static_cat
sequence: int64
- name: feat_dynamic_real
dtype: 'null'
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 77588260
num_examples: 370
- name: validation
num_bytes: 77730340
num_examples: 370
- name: test
num_bytes: 77872420
num_examples: 370
download_size: 123583004
dataset_size: 233191020
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
**Download the Dataset**:
```python
from datasets import load_dataset
dataset = load_dataset("LeoTungAnh/electricity_hourly")
```
**Dataset Card for Electricity Consumption**
This dataset encompasses hourly electricity consumption in kilowatts (kW) across a span of three years (2012-2014), involving 370 individual clients in Portugal.
**Preprocessing information**:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
**Dataset information**:
- Number of time series: 370
- Number of training samples: 26208
- Number of validation samples: 26256 (number_of_training_samples + 48)
- Number of testing samples: 26304 (number_of_validation_samples + 48)
**Dataset format**:
```python
Dataset({
features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
num_rows: 370
})
```
**Data format for a sample**:
- 'start': datetime.datetime
- 'target': list of a time series data
- 'feat_static_cat': time series index
- 'feat_dynamic_real': None
- 'item_id': name of time series
**Data example**:
```python
{'start': datetime.datetime(2012, 1, 1, 1, 0),
'target': [-0.19363673541224083, -0.08851588245610625, -0.19363673541224083, ... -0.5615597207587115,...],
'feat_static_cat': [0],
'feat_dynamic_real': None,
'item_id': 'MT_001'
}
```
**Usage**:
- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
- Other algorithms can extract data directly by making use of 'target' feature. |