Abstract

Time series classification is an important field in time series data-mining which have covered broad applications so far. Although it has attracted great interests during last decades, it remains a challenging task and falls short of efficiency due to the nature of its data: high dimensionality, large in data size and updating continuously. With the advent of deep learning, new methods have been developed, especially Convolutional Neural Network (CNN) models. In this paper, we present a review of our time series CNN approaches including: (i) a data-level approach based on encoding time series into frequency-domain signals via the Stockwell transform, (ii) an algorithm-level approach based on an adaptive convolutional layer filter that suits the time series in hand, and (iii) another algorithm-level approach adapted to time series classification tasks with limited annotated data, which is a global, fast and light-weight framework based on a transfer learning technique with a source learning task similar or different but related to the target learning task. These approaches are implemented on identifying human activities including normal movements of typical subjects and disorder-related movements such as stereotypical motor movements of autistic subjects. Experimental results show that our approaches improve performance of time series classification.

Highlights

  • Time series is a series of data points which are collected by recordinga set of observations chronologically

  • We propose a “Transfer learning with SVM read-out” framework which is composed of two parts: (i) the first part having first and intermediate layers’ weights of a Convolutional Neural Network (CNN) already pre-trained on a source learning task, and (ii) the second part composed of a support vector machine (SVM) classifier with RBF kernel which is connected to the end of the first part

  • Our data-level approach consists of encoding time series using STin order to produce noise-free input signals which offer a more efficient CNN training

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Summary

Introduction

Time series is a series of data points which are collected by recordinga set of observations chronologically. Examples of time series include speech, human activities, electrocardiogram (ECG), etc. Time series classification is widely applied in different fields such as in astronomy [1] to classify the brightness of a target star, in medical science to diagnose cardiac disorders [2] or to recognize human activities [3, 4], and in computer science for speech recognition [5, 6]. The local connectivity is achieved by replacing the weighted sums from the neural network with convolutions. As opposed to regular neural networks, all the values in the output feature map share the same weights so that all nodes in the output detect exactly the same pattern. The local connectivity and shared weights aspect of CNNs reduce the total number of learnable parameters, resulting in more efficient training and learning in each layer a weight matrix which is capable of capturing the necessary, translation-invariant features from the input

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