Pattern Sampling for Shapelet-based Time Series Classification

TL;DR

This paper introduces a pattern sampling method using a weighted trie to efficiently extract discriminative shapelets from discretized time series data, significantly reducing computational resources while maintaining accuracy.

Contribution

The paper proposes a novel pattern sampling approach with a weighted trie for shapelet discovery, improving efficiency over exhaustive search methods.

Findings

Reduces computational and memory requirements significantly.

Maintains competitive classification accuracy.

Faster runtime compared to previous algorithms.

Abstract

Subsequence-based time series classification algorithms provide accurate and interpretable models, but training these models is extremely computation intensive. The asymptotic time complexity of subsequence-based algorithms remains a higher-order polynomial, because these algorithms are based on exhaustive search for highly discriminative subsequences. Pattern sampling has been proposed as an effective alternative to mitigate the pattern explosion phenomenon. Therefore, we employ pattern sampling to extract discriminative features from discretized time series data. A weighted trie is created based on the discretized time series data to sample highly discriminative patterns. These sampled patterns are used to identify the shapelets which are used to transform the time series classification problem into a feature-based classification problem. Finally, a classification model can be trained using any off-the-shelf algorithm. Creating a pattern sampler requires a small number of patterns to be evaluated compared to an exhaustive search as employed by previous approaches. Compared to previously proposed algorithms, our approach requires considerably less computational and memory resources. Experiments demonstrate how the proposed approach fares in terms of classification accuracy and runtime performance.