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Computes the distance measure based on the cross-correlation between a pair of numeric time series. Usage CCorDistance(x, y, lag.max=(min(length(x), length(y))-1))
5 paź 2023 · Feature-based distances compute some feature of time series, such as discrete Fourier transforms or autocorrelation coefficients, and use either a specialized or common distance function (e.g., the Euclidean distance) to determine the distance between the computed features (Mori et al., 2016a).
29 gru 2015 · I want to calculate Dynamic Time Warping (DTW) distances in a dataframe. The result must be a new dataframe (a distance matrix) which includes the pairwise dtw distances among each row. For Euclidean Distance I use the following code:
In the next level, the wrapper function called TSDistances enables the calculation of distance measures between univariate time series objects of type ts, zoo and xts, the latter two defined in their respective packages: zoo (Zeileis and Grothendieck, 2005) and xts (Ryan and Ulrich, 2013).
31 sie 2020 · Dynamic time warping (DTW) is for temporal alignments. You are comparing non-temporal alignment by adding a constant between the two time series. Here is an example of temporal alignment by shifting 1 time unit between the two time series. The result is a DTW distance of 1.
18 wrz 2023 · In this paper, we propose two hyperparameter-free techniques to estimate the DTW distance between time series with missing values. The first technique, DTW-AROW, significantly decreases the impact of missing values on the DTW distance by modifying the optimization problem in the DTW algorithm.
30 maj 2020 · At a high level, our proposed distance measure will compute the distance between two time series TA and TB, by aggregating the distances between their All-subsequences sets. For this purpose, we need to find the nearest neighbor for each subsequence in A within B (and vice versa).
