TY - GEN
T1 - Time series classification using multi-channels deep convolutional neural networks
AU - Zheng, Yi
AU - Liu, Qi
AU - Chen, Enhong
AU - Ge, Yong
AU - Zhao, J. Leon
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84958543676&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958543676&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08010-9_33
DO - 10.1007/978-3-319-08010-9_33
M3 - Conference contribution
AN - SCOPUS:84958543676
SN - 9783319080093
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 298
EP - 310
BT - Web-Age Information Management - 15th International Conference, WAIM 2014, Proceedings
PB - Springer-Verlag
T2 - 15th International Conference on Web-Age Information Management, WAIM 2014
Y2 - 16 June 2014 through 18 June 2014
ER -