Multiobjective Learning in the Model Space for Time Series Classification

Zhichen GONG, Huanhuan CHEN, Bo YUAN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

56 Citations (Scopus)


A well-defined distance is critical for the performance of time series classification. Existing distance measurements can be categorized into two branches. One is to utilize handmade features for calculating distance, e.g., dynamic time warping, which is limited to exploiting the dynamic information of time series. The other methods make use of the dynamic information by approximating the time series with a generative model, e.g., Fisher kernel. However, previous distance measurements for time series seldom exploit the label information, which is helpful for classification by distance metric learning. In order to attain the benefits of the dynamic information of time series and the label information simultaneously, this paper proposes a multiobjective learning algorithm for both time series approximation and classification, termed multiobjective model-metric (MOMM) learning. In MOMM, a recurrent network is exploited as the temporal filter, based on which, a generative model is learned for each time series as a representation of that series. The models span a non-Euclidean space, where the label information is utilized to learn the distance metric. The distance between time series is then calculated as the model distance weighted by the learned metric. The network size is also optimized to learn parsimonious representations. MOMM simultaneously optimizes the data representation, the time series model separation, and the network size. The experiments show that MOMM achieves not only superior overall performance on uni/multivariate time series classification but also promising time series prediction performance. © 2013 IEEE.
Original languageEnglish
Article number8264689
Pages (from-to)918-932
Number of pages15
JournalIEEE Transactions on Cybernetics
Issue number3
Early online date19 Jan 2018
Publication statusPublished - Mar 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB1000905, in part by the National Natural Science Foundation of China under Grant 61673363, Grant 91546116, Grant 61329302, and Grant 61503357, and in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284 and Grant JCYJ20170307105521943. This paper was recommended by Associate Editor H. Wang.


  • Echo state network (ESN)
  • learning in the model space
  • multiobjective learning
  • time series classification


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