Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends

  • Harya Widiputra
  • Russel Pears
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6635)

Abstract

Time-series prediction has been very well researched by both the Statistical and Data Mining communities. However the multiple time-series problem of predicting simultaneous movement of a collection of time sensitive variables which are related to each other has received much less attention. Strong relationships between variables suggests that trajectories of given variables that are involved in the relationships can be improved by including the nature and strength of these relationships into a prediction model. The key challenge is to capture the dynamics of the relationships to reflect changes that take place continuously over time. In this research we propose a novel algorithm for extracting profiles of relationships through an evolving clustering method. We use a form of non-parametric regression analysis to generate predictions based on the profiles extracted and historical information from the past. Experimental results on a real-world climatic data reveal that the proposed algorithm outperforms well established methods of time-series prediction.

Keywords

time-series inter-relationships multiple time-series prediction evolving clustering method recurring trends 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Harya Widiputra
    • 1
  • Russel Pears
    • 1
  • Nikola Kasabov
    • 1
  1. 1.The Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand

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