Volume 10 Number 1 (Jan. 2015)
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JCP 2015 Vol.10(1): 1-11 ISSN: 1796-203X
doi: 10.17706/jcp.10.1.1-11

Multi-dimensional Time Series Approximation Using Local Features at Thinned-out Keypoints

Yu Fang, Do Xuan Huy, Hung-Hsuan Huang, Kyoji Kawagoe
Ritsumeikan University, Kusatsu, Shiga, Japan.
Abstract—A multi-dimensional time-series is a sequence of vectors measured by many devices at points in time. Although many methods have been proposed to model and classify the data, these methods lead to a problematic relationship between cost and accuracy. In this paper, we propose a novel method for approximating multi-dimensional time-series, named multi-dimensional time-series Approximation with use of Local features at Thinned-out Keypoints (A-LTK), which enables an adequate accuracy value to be obtained even when reduced storage cost is a requirement. The main concepts of A-LTK are 1) reduction of time points and 2) construction of local features at the thinned-out keypoints. A preliminary evaluation indicated that with these points our proposed method was capable of achieving almost the same accuracy with less storage cost, compared to existing methods.

Index Terms—Multi-dimensions, times series, classification, approximation, keypoint extraction, local features.

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Cite: Yu Fang, Do Xuan Huy, Hung-Hsuan Huang, Kyoji Kawagoe, "Multi-dimensional Time Series Approximation Using Local Features at Thinned-out Keypoints," Journal of Computers vol. 10, no. 1, pp. 1-11, 2015.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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