Abstract
Wavelet transform is a well-known multi-resolution tool to analyze the time series in the time-frequency domain. Wavelet basis is diverse but predefined by manual without taking the data into the consideration. Hence, it is a great challenge to select an appropriate wavelet basis to separate the low and high frequency components for the task on the hand. Inspired by the lifting scheme in the second-generation wavelet, the updater and predictor are learned directly from the time series to separate the low and high frequency components of the time series. An adaptive multi-scale wavelet neural network (AMSW-NN) is proposed for time series classification in this paper. First, candidate frequency decompositions are obtained by a multi-scale convolutional neural network in conjunction with a depthwise convolutional neural network. Then, a selector is used to choose the optimal frequency decomposition from the candidates. At last, the optimal frequency decomposition is fed to a classification network to predict the label. A comprehensive experiment is performed on the UCR archive. The results demonstrate that, compared with the classical wavelet transform, AMSW-NN could improve the performance based on different classification networks.
Highlights
In recent years, the research on time series classification has achieved unprecedented prosperity [1]
The methods for time series classification can be divided into two categories:timedomain methods and frequency-domain methods [6]
DW-Fully Convolutional Network (FCN), DW-ResNet, and DW-Inception are the abbreviations of db4 decomposition with FCN, ResNet, and Inception, respectively
Summary
The research on time series classification has achieved unprecedented prosperity [1]. To better compare different researches for time series classification, UCR archive [5] is built and there are at least one thousand published papers making use of at least one dataset from this archive. The methods for time series classification can be divided into two categories:timedomain methods and frequency-domain methods [6]. Time-domain methods such as shapelets [7] and elastic distance measures [8] consider the shape of time series is important to the classification. This section briefly introduces the lifting scheme in the second-generation wavelet which is the building block of the proposed method. Compared with the classical wavelet ( called the first-generation wavelet), the lifting wavelet does not rely on the Fourier transform. The lifting scheme is usually divided into three steps including split, prediction, and update. The update-first structure is used in the proposed method due to the stability [18] and described
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