Robust prediction in nearly periodic time series using motifs

Woon Huei Chai, Hongliang Guo, Shen Shyang Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we consider the prediction task for a process with nearly periodic property, i.e., patterns occur with some regularities but no exact periodicity. We propose an inference approach based on probabilistic Markov framework utilizing motif-driven transition probabilities for sequential prediction. In particular, a Markov-based weighting framework utilizing fully the information from recent historical data and sequential pattern regularities is developed for nearly periodic time series prediction. Preliminary experimental results show that our prediction approach is competitive against the moving average and multi-layer perceptron neural network approaches on synthetic data. Moreover, our proposed method is shown to be empirically robust on time-series with missing data and noise. We also demonstrate the usefulness of our proposed approach on a real-world vehicle parking lot availability prediction task.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2003-2010
Number of pages8
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - Sep 3 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period7/6/147/11/14

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Chai, W. H., Guo, H., & Ho, S. S. (2014). Robust prediction in nearly periodic time series using motifs. In Proceedings of the International Joint Conference on Neural Networks (pp. 2003-2010). [6889797] (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889797