A Dictionary-based approach to Time Series Ordinal Classification

TL;DR

This paper introduces an ordinal adaptation of the state-of-the-art dictionary-based Time Series Classification method, demonstrating improved performance on ordinal time series problems through extensive experiments.

Contribution

The paper presents O-TDE, the first ordinal dictionary-based TSC method, and shows its superiority over existing nominal techniques on 18 TSOC datasets.

Findings

O-TDE outperforms four existing dictionary-based methods.

Experiments on 18 TSOC problems show significant improvements.

Ordinal approach leverages label ordering for better accuracy.

Abstract

Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.