Multivariate time series early classification using multi-domain deep neural network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Early classification on multivariate time series is an important research topic in data mining with wide applications to various domains like medical diagnosis, motion detection and financial prediction, etc. Shapelet is probably one of the most commonly used approaches to tackle early classification problem, but one drawback of shaplet is its inefficiency. More importantly, the extracted shapelets may not be applicable to every test case at any time point. This work focuses on early classification of multivariate time series and proposes a novel framework named Multi-Domain Deep Neural Network (MDDNN), in which convolutional neural network (CNN) and long-short term memory (LSTM) are incorporated to learn feature representation and relationship embedding in the long sequences with long time lags. The proposed model can make predictions at any time point of a multivariate time series with the help of a truncation process. We conducted experiments on four real datasets and compared with state-of-the-art algorithms. The experimental results indicate that the proposed method outperforms the alternatives significantly on both of earliness and accuracy. Detailed analysis about the proposed model is also provided in this work. To the best of our knowledge, this is the first work that incorporates deep neural network methods (CNN and LSTM) and multi-domain approach to boost the problem of early classification on multivariate time series.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018
EditorsTina Eliassi-Rad, Wei Wang, Ciro Cattuto, Foster Provost, Rayid Ghani, Francesco Bonchi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages90-98
Number of pages9
ISBN (Electronic)9781538650905
DOIs
StatePublished - 31 Jan 2019
Event5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018 - Turin, Italy
Duration: 1 Oct 20184 Oct 2018

Publication series

NameProceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018

Conference

Conference5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018
CountryItaly
CityTurin
Period1/10/184/10/18

Keywords

  • Convolutional Neural Networks
  • Early Classification
  • LSTM
  • Multi-domain Inputs
  • Time Series Analysis

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