Model-Based Time Series Classification

  • Alexios Kotsifakos
  • Panagiotis Papapetrou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)

Abstract

We propose MTSC, a filter-and-refine framework for time series Nearest Neighbor (NN) classification. Training time series belonging to certain classes are first modeled through Hidden Markov Models (HMMs). Given an unlabeled query, and at the filter step, we identify the top K models that have most likely produced the query. At the refine step, a distance measure is applied between the query and all training time series of the top K models. The query is then assigned with the class of the NN. In our experiments, we first evaluated the NN classification error rate of HMMs compared to three state-of-the-art distance measures on 45 time series datasets of the UCR archive, and showed that modeling time series with HMMs achieves lower error rates in 30 datasets and equal error rates in 4. Secondly, we compared MTSC with Cross Validation defined over the three measures on 33 datasets, and we observed that MTSC is at least as good as the competitor method in 23 datasets, while achieving competitive speedups, showing its effectiveness and efficiency.

Keywords

Time Series Distance Measure Hide Markov Model Cross Validation Near Neighbor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexios Kotsifakos
    • 1
  • Panagiotis Papapetrou
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonUSA
  2. 2.Department of Computer and Systems SciencesStockholm UniversitySweden

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