Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering

TL;DR

This paper introduces a generalized time warping invariant dictionary learning method for time series classification and clustering, addressing DTW limitations by using continuous warping operators and joint optimization.

Contribution

It proposes a novel continuous-time warping operator integrated into dictionary learning, improving alignment and classification accuracy over traditional DTW-based methods.

Findings

Outperforms benchmark methods in classification accuracy.

Effectively handles temporal misalignments in time series.

Validated on ten public datasets.

Abstract

Dictionary learning is an effective tool for pattern recognition and classification of time series data. Among various dictionary learning techniques, the dynamic time warping (DTW) is commonly used for dealing with temporal delays, scaling, transformation, and many other kinds of temporal misalignments issues. However, the DTW suffers overfitting or information loss due to its discrete nature in aligning time series data. To address this issue, we propose a generalized time warping invariant dictionary learning algorithm in this paper. Our approach features a generalized time warping operator, which consists of linear combinations of continuous basis functions for facilitating continuous temporal warping. The integration of the proposed operator and the dictionary learning is formulated as an optimization problem, where the block coordinate descent method is employed to jointly optimize warping paths, dictionaries, and sparseness coefficients. The optimized results are then used as hyperspace distance measures to feed classification and clustering algorithms. The superiority of the proposed method in terms of dictionary learning, classification, and clustering is validated through ten sets of public datasets in comparing with various benchmark methods.