Scalable dictionary classifiers for time series classification

Abstract

Dictionary based classifiers are a family of algorithms for time series classification (TSC) that focus on capturing the frequency of pattern occurrences in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as well as the best performing dictionary based classifier. However, BOSS does not scale well. We evaluate changes to the way BOSS chooses classifiers for its ensemble, replacing its parameter search with random selection. This change allows for the easy implementation of contracting (setting a build time limit for the classifier) and check-pointing (saving progress during the classifiers build). We achieve a significant reduction in build time without a significant change in accuracy on average when compared to BOSS by creating a fixed size weighted ensemble selecting the best performers from a randomly chosen parameter set. Our experiments are conducted on datasets from the recently expanded UCR time series archive. We demonstrate the usability improvements to randomised BOSS with a case study using a large whale acoustics dataset for which BOSS proved infeasible.