load_italy_power_demand(split=None, return_X_y=True)[source]#

Load ItalyPowerDemand time series classification problem.

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=True)

If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.

X: pd.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


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


>>> from sktime.datasets import load_italy_power_demand
>>> X, y = load_italy_power_demand()