sktime.datasets.base.load_acsf1

sktime.datasets.base.load_acsf1(split=None, return_X_y=False)[source]

Loads the power consumption of typical appliances 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 (1460)

  • Train cases (100)

  • Test cases (100)

  • Number of classes (10)

  • The dataset contains the power consumption of typical appliances.

  • The recordings are characterized by long idle periods and some high bursts

  • of energy consumption when the appliance is active.

  • The classes correspond to 10 categories of home appliances;

  • mobile phones (via chargers), coffee machines, computer stations

  • (including monitor), fridges and freezers, Hi-Fi systems (CD players),

  • lamp (CFL), laptops (via chargers), microwave ovens, printers, and

  • televisions (LCD or LED).”

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

  • =ACSF1