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Evolving integrated multi-model framework for on line multiple time series prediction

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Abstract

Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in time varying amounts continuously over time. In this paper we describe the use of a novel integrated multi-model framework (IMMF) that combined models developed at three different levels of data granularity, namely the global, local and transductive models to perform multiple time series prediction. The IMMF is implemented by training a neural network to assign relative weights to predictions from the models at the three different levels of data granularity. Our experimental results indicate that IMMF significantly outperforms well established methods of time series prediction when applied to the multiple time series prediction problem.

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Acknowledgements

The work was funded by the Knowledge Engineering and Discovery Research Institute (KEDRI, http://www.kedri.info) and the School of Computing and Mathematical Sciences of the Auckland University of Technology, New Zealand.

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Authors and Affiliations

  1. School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

    Russel Pears

  2. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

    Harya Widiputra & Nikola Kasabov

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  1. Russel Pears
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  2. Harya Widiputra
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  3. Nikola Kasabov
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Correspondence to Russel Pears.

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Pears, R., Widiputra, H. & Kasabov, N. Evolving integrated multi-model framework for on line multiple time series prediction. Evolving Systems 4, 99–117 (2013). https://doi.org/10.1007/s12530-012-9069-y

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