Features or Shape? Tackling the False Dichotomy of Time Series Classification

TL;DR

This paper introduces a novel time series classification model that adaptively combines shape-based and feature-based methods, automatically selecting the best approach for each class to improve accuracy.

Contribution

It proposes an adaptive model that dynamically chooses between shape and feature-based classification for different classes, addressing the limitations of using either method alone.

Findings

Significant improvement in classification accuracy on real datasets

The model effectively discriminates classes using the most suitable approach

Statistically significant results compared to traditional methods

Abstract

Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have used either shape-based classification using a distance measure or feature-based classification after finding some suitable features for the domain. It seems to be underappreciated that in many datasets it is the case that some classes are best discriminated with features, while others are best discriminated with shape. Thus, making the shape vs. feature choice will condemn us to poor results, at least for some classes. In this work, we propose a new model for classifying time series that allows the use of both shape and feature-based measures, when warranted. Our algorithm automatically decides which approach is best for which class, and at query time chooses which classifier to trust the most. We evaluate our idea on real world datasets and demonstrate that our ideas produce statistically significant improvement in classification accuracy.