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@@ -47,17 +47,17 @@ dataset = load_dataset("LeoTungAnh/traffic_hourly")
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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
@@ -65,7 +65,7 @@ This dataset encompasses hourly electricity consumption in kilowatts (kW) across
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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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  ```
@@ -84,15 +84,11 @@ This dataset encompasses hourly electricity consumption in kilowatts (kW) across
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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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-
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- 'target': [-0.19363673541224083, -0.08851588245610625, -0.19363673541224083, ... -0.5615597207587115,...],
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-
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  'feat_static_cat': [0],
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-
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  'feat_dynamic_real': None,
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-
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- 'item_id': 'MT_001'
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  }
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  ```
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  **Dataset Card for Electricity Consumption**
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+ this dataset encompasses 862 hourly time series data points revealing the road occupancy rates across freeways in the San Francisco Bay area from 2015 to 2016.
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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: 862
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+ - Number of training samples: 17448
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+ - Number of validation samples: 17496 (number_of_training_samples + 48)
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+ - Number of testing samples: 17544 (number_of_validation_samples + 48)
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  **Dataset format**:
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  ```python
 
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  features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
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+ num_rows: 862
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  })
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  ```
 
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  **Data example**:
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  ```python
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+ {'start': datetime.datetime(2015, 1, 1, 0, 0, 1),
 
 
 
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  'feat_static_cat': [0],
 
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  'feat_dynamic_real': None,
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+ 'item_id': 'T1',
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+ 'target': [-0.7127609544951682, -0.6743409178438863, -0.3749847989359815, ... 0.12447567753068307,...]
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  }
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  ```
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