Affine and Regional Dynamic Time Warpng
This work addresses time series alignment challenges for applications requiring robustness to amplitude variations and local region emphasis, though it appears incremental by extending existing DTW methods.
The authors tackled the problem of aligning time series with invariance to amplitude scaling/offset and regional emphasis by proposing affine DTW (ADTW) and regional DTW (RDTW), which outperform DTW on simulated datasets and achieve competitive classification results with state-of-the-art methods on real datasets.
Pointwise matches between two time series are of great importance in time series analysis, and dynamic time warping (DTW) is known to provide generally reasonable matches. There are situations where time series alignment should be invariant to scaling and offset in amplitude or where local regions of the considered time series should be strongly reflected in pointwise matches. Two different variants of DTW, affine DTW (ADTW) and regional DTW (RDTW), are proposed to handle scaling and offset in amplitude and provide regional emphasis respectively. Furthermore, ADTW and RDTW can be combined in two different ways to generate alignments that incorporate advantages from both methods, where the affine model can be applied either globally to the entire time series or locally to each region. The proposed alignment methods outperform DTW on specific simulated datasets, and one-nearest-neighbor classifiers using their associated difference measures are competitive with the difference measures associated with state-of-the-art alignment methods on real datasets.