A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers

TL;DR

This paper compares four dictionary-based time series classification algorithms, demonstrating improved accuracy over previous methods but highlighting trade-offs in computational cost, and discusses potential avenues for future enhancements.

Contribution

It provides a comprehensive comparison of four dictionary-based TSC algorithms, updating prior benchmarks and analyzing their performance, efficiency, and potential improvements.

Findings

Improved accuracy over previous best methods.

Trade-offs between performance and computational cost.

Potential paths for algorithmic improvement.

Abstract

Time series classification (TSC) is the problem of learning labels from time dependent data. One class of algorithms is derived from a bag of words approach. A window is run along a series, the subseries is shortened and discretised to form a word, then features are formed from the histogram of frequency of occurrence of words. We call this type of approach to TSC dictionary based classification. We compare four dictionary based algorithms in the context of a wider project to update the great time series classification bakeoff, a comparative study published in 2017. We experimentally characterise the algorithms in terms of predictive performance, time complexity and space complexity. We find that we can improve on the previous best in terms of accuracy, but this comes at the cost of time and space. Alternatively, the same performance can be achieved with far less cost. We review the relative merits of the four algorithms before suggesting a path to possible improvement.