sliding_window

dtaianomaly.windowing.sliding_window(X: ndarray, window_size: int, stride: int) → ndarray

Construct a sliding window for the given time series.

Convert the given time series into sliding windows of given size, using the given stride.

Parameters:

Xarray-like of shape (n_samples, n_attributes)

The time series.

window_sizeint

The window size for the sliding windows.

strideint

The stride, i.e., the step size for the windows.

Returns:

np.ndarray of shape ((n_samples - window_size)/stride + 1, n_attributes * window_size)

The windows as a 2D numpy array. Each row corresponds to a window. For windows of multivariate time series are flattened to form a 1D array of length the number of attributes multiplied by the window size.

Examples

>>> import numpy as np
>>> from dtaianomaly.windowing import sliding_window
>>> X = np.array([0.2, 0.3, 0.5, 0.8, 0.9, 0.6, 0.2, 0.1])
>>> sliding_window(X, 2, 1)
array([[0.2, 0.3],
       [0.3, 0.5],
       [0.5, 0.8],
       [0.8, 0.9],
       [0.9, 0.6],
       [0.6, 0.2],
       [0.2, 0.1]])
>>> sliding_window(X, 3, 1)
array([[0.2, 0.3, 0.5],
       [0.3, 0.5, 0.8],
       [0.5, 0.8, 0.9],
       [0.8, 0.9, 0.6],
       [0.9, 0.6, 0.2],
       [0.6, 0.2, 0.1]])
>>> sliding_window(X, 3, 2)
array([[0.2, 0.3, 0.5],
       [0.5, 0.8, 0.9],
       [0.9, 0.6, 0.2],
       [0.6, 0.2, 0.1]])