Application of Discrete Wavelet Transform in Shapelet-Based Classification

Lijuan Yan,Yanshen Liu,Yi Liu

doi:10.1155/2020/6523872

Lijuan Yan, Yanshen Liu + Show 1 more

Open Access

https://doi.org/10.1155/2020/6523872

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Abstract

Recently, several shapelet-based methods have been proposed for time series classification, which are accomplished by identifying the most discriminating subsequence. However, for time series datasets in some application domains, pattern recognition on the original time series cannot always obtain ideal results. To address this issue, we propose an ensemble algorithm by combining time frequency analysis and shape similarity recognition of time series. Discrete wavelet transform is used to decompose the time series into different components, and the shapelet features are identified for each component. According to the different correlations between each component and the original time series, an ensemble classifier is built by weighted majority voting, and the Monte Carlo method is used to search for optimal weight vector. The comparative experiments and sensitivity analysis are conducted on 25 datasets from UCR Time Series Classification Archive, which is an important open dataset resource in time series mining. The results show the proposed method has a better performance in terms of accuracy and stability than the compared classifiers.

Highlights

A time series is a data sequence that represents recorded values of a phenomenon over time
Data such as meteorological data in weather forecast, floating currency exchange rate in foreign trade, radio wave, images captured by medical devices, and continuous signals in engineering applications can be regarded as time series [2]
Time series data are more complex to analyse than the cross-sectional data due to the way in which measurements change over time [3]

Summary

Introduction

A time series is a data sequence that represents recorded values of a phenomenon over time. Lines et al [12] proposed a time series classification method based on shapelet transformation (ST). Is method creates new classification data before constructing the classifier, so that it keeps the explanatory power of shapelets and improves simultaneously the accuracy of classification. Ese methods combined multiple subclassifications, such as distance measure, shapelet identification, spectrum analysis, other time series feature representation, and transformation strategies. In this study, combining with the advantages of the DWT and shapelet approach, we propose a new ensemble method, which embeds the DWT into shapelet-discovery algorithm to get a transformed data and implements an ensemble classifier to train and test the transformed data. Ese shapelet sets reflect the corresponding classification characteristics and are used to convert the original time series into feature vector representations .

Related Definitions

The Proposed Method

Experimental Settings

Findings

Conclusions