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An OGS-Based Dynamic Time Warping Algorithm for Time Series Data

In: Innovation in the High-Tech Economy

Author

Listed:
  • Mi Zhou

    (Jinan University)

Abstract

Dynamic Time Warping (DTW) is a powerful technique in the time-series similarity search. However, its performance on large-scale data is unsatisfactory because of its high computational cost. Although many methods have been proposed to alleviate this, they are mostly in- direct methods, i.e., they do not improve the DTW algorithm itself. In this paper, we propose to incorporate the Ordered Graph Search (OGS) and the lower bound for DTW into an improved DTW algorithm and apply it on time series data. Extensive experiments show that the improved DTW algorithm is faster than the original dynamic programming based algorithm on multi-dimensional time series data. It is also especially useful in the post-processing stage of searching in large time series data based on DTW distance.

Suggested Citation

  • Mi Zhou, 2013. "An OGS-Based Dynamic Time Warping Algorithm for Time Series Data," Contributions to Economics, in: Pang Chuan & Vasil Khachidze & Ivan K.W. Lai & Yide Liu & Sohail Siddiqui & Tim Wang (ed.), Innovation in the High-Tech Economy, edition 127, pages 115-121, Springer.
    • Handle: RePEc:spr:conchp:978-3-642-41585-2_10
    DOI: 10.1007/978-3-642-41585-2_10
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