Bag of recurrence patterns representation 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.

Image-Based Methods to Investigate Synchronization between Time Series Relevant for Plasma Fusion Diagnostics

“…Other promising approaches have been proposed recently. A time series imaging encoding scheme called Motif Difference Field (MDF), based on the motifs of different lengths, has been described in [ 16 ]. The approach is particularly useful for time series clustering allowing higher-order patterns or structures discovery in time series data.…”

Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal

“…Hatami et al in Reference [39] used RP as an input to CNN for TSC problems. In a subsequent paper [40], the authors used bag of feature concepts on recurrence plot and generated bag of recurrence patterns for representation of time series for classification with Support Vector Machine (SVM) classifier. Michael et al [41] defined a cross recurrence plot (CRP) as an extension of recurrence plot to visualize similar recurring patterns in two time series and proposed another similarity measure called the cross recurrence plot compression distance (CRPCD), which is a modification of the work in Reference [38].…”

Encoding Time Series as Multi-Scale Signed Recurrence Plots for Classification Using Fully Convolutional Networks

“…Firstly, sequences of different datasets vary significantly in length and their distinctive regions usually distribute on various scales. Existing methods deal with this problem only by adjusting the image size [26,[32][33][34]. However, to avoid high computational overhead, the adjustable size is limited to a small range, which often decreases the representation ability of RP.…”

“…RP is a widely used visualization technique for analyzing dynamical systems [ 30 , 31 ]. Due to the graphical nature of exposing hidden patterns and local correlation information of a sequence, RP has been introduced to TSC for representing time series as images [ 26 , 32 , 33 ]. However, several defects limit its further application in TSC.…”

“…However, to avoid high computational overhead, the adjustable size is limited to a small range, which often decreases the representation ability of RP. We address this challenge by additionally introducing phase space dimension ( m ) and embedding time delay ( ) of RP, which are fixed in other methods [ 26 , 32 , 33 , 34 ]. Different values of m , , and image sizes are explored according to different datasets in order to enrich the scales of RP images.…”