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Wavelet Space Partitioning for Symbolic Time Series Analysis |
| Venkatesh Rajagopalan;Asok Ray |
| Department of Mechanical and Nuclear Engineering, College of Engineering, The Pennsylvania State University, University Park, PA 16802-1412, USA |
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| Cite this article: |
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Venkatesh Rajagopalan, Asok Ray 2006 Chin. Phys. Lett. 23 1951-1954 |
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Abstract A crucial step in symbolic time series analysis (STSA) of observed data is symbol sequence generation that relies on partitioning the phase-space of the underlying dynamical system. We present a novel partitioning method, called wavelet-space (WS) partitioning, as an alternative to symbolic false nearest neighbour (SFNN) partitioning. While the WS and SFNN partitioning methods have been demonstrated to yield comparable performance for anomaly detection on laboratory apparatuses, computation of WS partitioning is several orders of magnitude faster than that of the SFNN partitioning.
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| Keywords:
89.75.-k
89.70.+c
07.90.+c
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Published: 01 July 2006
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| PACS: |
89.75.-k
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(Complex systems)
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89.70.+c
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07.90.+c
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(Other topics in instruments, apparatus, and components common to several branches of physics and astronomy)
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