Create README.md

README.md

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+---
+license: openrail
+task_categories:
+- time-series-forecasting
+size_categories:
+- 1K<n<10K
+---
+**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.