sktime.datasets.base.load_italy_power_demand

sktime.datasets.base.load_italy_power_demand(split=None, return_X_y=False)[source]

Loads the ItalyPowerDemand time series classification problem and returns X and y

Parameters
  • split (None or str{"train", "test"}, optional (default=None)) – Whether to load the train or test partition of the problem. By default it loads both.

  • return_X_y (bool, optional (default=False)) – If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.

Returns

  • X (pandas DataFrame with m rows and c columns) – The time series data for the problem with m cases and c dimensions

  • y (numpy array) – The class labels for each case in X

  • Details

  • ——-

  • Dimensionality (univariate)

  • Series length (24)

  • Train cases (67)

  • Test cases (1029)

  • Number of classes (2)

  • The data was derived from twelve monthly electrical power demand time

  • series from Italy and

  • first used in the paper “Intelligent Icons (Integrating Lite-Weight Data)

  • Mining and

  • Visualization into GUI Operating Systems”. The classification task is to

  • distinguish days

  • from Oct to March (inclusive) from April to September.

  • Dataset details (http://timeseriesclassification.com/description.php)

  • ?Dataset=ItalyPowerDemand