metadata

dataset_info:
  features:
    - name: start
      dtype: timestamp[s]
    - name: feat_static_cat
      sequence: uint64
    - name: feat_dynamic_real
      sequence:
        sequence: float32
    - name: item_id
      dtype: string
    - name: target
      sequence: float64
  splits:
    - name: train
      num_bytes: 120352440
      num_examples: 862
    - name: validation
      num_bytes: 120683448
      num_examples: 862
    - name: test
      num_bytes: 121014456
      num_examples: 862
  download_size: 124542918
  dataset_size: 362050344
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for "traffic_hourly"

More Information needed

Download the Dataset:

from datasets import load_dataset

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.

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:

  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:

{'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.