load_italy_power_demand

load_italy_power_demand(split=None, return_X_y=True, return_type=None)

Load ItalyPowerDemand time series classification problem.

Parameters:

split: None or one of “TRAIN”, “TEST”, optional (default=None)

Whether to load the train or test instances of the problem. By default it loads both train and test instances (in a single container).

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.

return_type: valid Panel mtype str or None, optional (default=None=”nested_univ”)

Memory data format specification to return X in, None = “nested_univ” type. str can be any supported sktime Panel mtype,

for list of mtypes, see datatypes.MTYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

commonly used specifications:

“nested_univ: nested pd.DataFrame, pd.Series in cells “numpy3D”/”numpy3d”/”np3D”: 3D np.ndarray (instance, variable, time index) “numpy2d”/”np2d”/”numpyflat”: 2D np.ndarray (instance, time index) “pd-multiindex”: pd.DataFrame with 2-level (instance, time) MultiIndex

Exception is raised if the data cannot be stored in the requested type.

Returns:

X: sktime data container, following mtype specification return_type

The time series data for the problem, with n instances

y: 1D numpy array of length n, only returned if return_X_y if True

The class labels for each time series instance in X If return_X_y is False, y is appended to X instead.

Notes

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

Examples

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