wdtw_distance#
- wdtw_distance(x: ndarray, y: ndarray, window: float | None = None, itakura_max_slope: float | None = None, bounding_matrix: ndarray = None, g: float = 0.05, **kwargs: Any) float [source]#
Compute the weighted dynamic time warping (WDTW) distance between time series.
First proposed in [1], WDTW adds a adds a multiplicative weight penalty based on the warping distance. This means that time series with lower phase difference have a smaller weight imposed (i.e less penalty imposed) and time series with larger phase difference have a larger weight imposed (i.e. larger penalty imposed).
Formally this can be described as:
\[d_{w}(x_{i}, y_{j}) = ||w_{|i-j|}(x_{i} - y_{j})||\]Where d_w is the distance with a the weight applied to it for points i, j, where w(|i-j|) is a positive weight between the two points x_i and y_j.
- Parameters:
- x: np.ndarray (1d or 2d array)
First time series.
- y: np.ndarray (1d or 2d array)
Second time series.
- window: float, defaults = None
Float that is the radius of the sakoe chiba window (if using Sakoe-Chiba lower bounding). Value must be between 0. and 1.
- itakura_max_slope: float, defaults = None
Gradient of the slope for itakura parallelogram (if using Itakura Parallelogram lower bounding). Value must be between 0. and 1.
- bounding_matrix: np.ndarray (2d of size mxn where m is len(x) and n is len(y)),
defaults = None
Custom bounding matrix to use. If defined then other lower_bounding params are ignored. The matrix should be structure so that indexes considered in bound should be the value 0. and indexes outside the bounding matrix should be infinity.
- g: float, defaults = 0.
Constant that controls the curvature (slope) of the function; that is, g controls the level of penalisation for the points with larger phase difference.
- kwargs: Any
Extra kwargs.
- Returns:
- float
Wdtw distance between the x and y.
- Raises:
- ValueError
If the sakoe_chiba_window_radius is not a float. If the itakura_max_slope is not a float. If the value of x or y provided is not a numpy array. If the value of x or y has more than 2 dimensions. If a metric string provided, and is not a defined valid string. If a metric object (instance of class) is provided and doesn’t inherit from NumbaDistance. If the metric type cannot be determined If both window and itakura_max_slope are set
References
[1]Young-Seon Jeong, Myong K. Jeong, Olufemi A. Omitaomu, Weighted dynamic time
warping for time series classification, Pattern Recognition, Volume 44, Issue 9, 2011, Pages 2231-2240, ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2010.09.022.
Examples
>>> import numpy as np >>> from sktime.distances import wdtw_distance >>> x_1d = np.array([1, 2, 3, 4]) # 1d array >>> y_1d = np.array([5, 6, 7, 8]) # 1d array >>> wdtw_distance(x_1d, y_1d) 27.975712863958133
>>> x_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) # 2d array >>> y_2d = np.array([[9, 10, 11, 12], [13, 14, 15, 16]]) # 2d array >>> wdtw_distance(x_2d, y_2d) 243.2106560107827