Time series classification using multi-channels deep convolutional neural networks

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages298-310
Volume8485 LNCS
ISBN (Print)9783319080093
StatePublished - 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8485 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title15th International Conference on Web-Age Information Management (WAIM 2014)
PlaceChina
CityMacau
Period16 - 18 June 2014

Abstract

Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification. © 2014 Springer International Publishing Switzerland.

Citation Format(s)

Time series classification using multi-channels deep convolutional neural networks. / Zheng, Yi; Liu, Qi; Chen, Enhong; Ge, Yong; Zhao, J. Leon.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8485 LNCS Springer Verlag, 2014. p. 298-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8485 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)