Update README.md

README.md

@@ -47,17 +47,17 @@ dataset = load_dataset("LeoTungAnh/traffic_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.
+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.
 
 **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)
+- Number of time series: 862
+- Number of training samples: 17448
+- Number of validation samples: 17496 (number_of_training_samples + 48)
+- Number of testing samples: 17544 (number_of_validation_samples + 48)
 
 **Dataset format**:
 ```python
@@ -65,7 +65,7 @@ This dataset encompasses hourly electricity consumption in kilowatts (kW) across
   
       features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
       
-      num_rows: 370
+      num_rows: 862
       
   })
 ```
@@ -84,15 +84,11 @@ This dataset encompasses hourly electricity consumption in kilowatts (kW) across
 
 **Data example**:
 ```python
-{'start': datetime.datetime(2012, 1, 1, 1, 0),
-
- 'target': [-0.19363673541224083, -0.08851588245610625, -0.19363673541224083, ... -0.5615597207587115,...],
- 
+{'start': datetime.datetime(2015, 1, 1, 0, 0, 1),
  'feat_static_cat': [0],
- 
  'feat_dynamic_real': None,
- 
- 'item_id': 'MT_001'
+ 'item_id': 'T1',
+ 'target': [-0.7127609544951682, -0.6743409178438863, -0.3749847989359815, ... 0.12447567753068307,...]
 }
 ```