Bag of Recurrence Patterns Representation for Time-Series Classification

TL;DR

This paper introduces a novel approach for time-series classification by transforming signals into recurrence plot images and applying a bag of features model, resulting in improved accuracy over existing methods.

Contribution

It proposes embedding recurrence plots into the bag of features framework for the first time, enabling texture-based analysis of time-series data.

Findings

Significant accuracy improvement over existing BoF models.

Outperforms state-of-the-art algorithms on UCI archive.

Enables texture recognition techniques for time-series classification.

Abstract

Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag of Features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of "feature words" of a data-learned dictionary. This paper proposes embedding the Recurrence Plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC. While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them. Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treats TSC task as a texture recognition problem. Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed Bag of Recurrence patterns (BoR), compared not only to the existing BoF models, but also to the state-of-the art algorithms.