ddtw_distance#
- ddtw_distance(x: ndarray, y: ndarray, window: float | None = None, itakura_max_slope: float | None = None, bounding_matrix: ndarray = None, compute_derivative=None, **kwargs: Any) float [source]#
Compute the derivative dynamic time warping (DDTW) distance between time series.
DDTW is an adaptation of DTW originally proposed in [1]. DDTW attempts to improve on dtw by better account for the ‘shape’ of the time series. This is done by considering y axis data points as higher level features of ‘shape’. To do this the first derivative of the sequence is taken, and then using this derived sequence a dtw computation is done.
The default derivative used is:
\[D_{x}[q] = \frac{{}(q_{i} - q_{i-1} + ((q_{i+1} - q_{i-1}/2)}{2}\]Where q is the original time series and d_q is the derived time series.
- 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.
- compute_derivative: Callable[[np.ndarray], np.ndarray],
defaults = average slope difference
Callable that computes the derivative. If none is provided the average of the slope between two points used.
- kwargs: Any
Extra kwargs.
- Returns:
- float
Ddtw 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 a resolved metric or compute derivative callable is not no_python compiled. If the metric type cannot be determined If the compute derivative callable is not no_python compiled. If both window and itakura_max_slope are set
References
[1]Keogh, Eamonn & Pazzani, Michael. (2002). Derivative Dynamic Time Warping. First SIAM International Conference on Data Mining. 1. 10.1137/1.9781611972719.1.
Examples
>>> import numpy as np >>> from sktime.distances import ddtw_distance >>> x_1d = np.array([1, 2, 3, 4]) # 1d array >>> y_1d = np.array([5, 6, 7, 8]) # 1d array >>> ddtw_distance(x_1d, y_1d) 0.0
>>> 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 >>> ddtw_distance(x_2d, y_2d) 0.0