Applications of shapelet transform to time series classification of earthquake, wind and wave data

• URL: http://arxiv.org/abs/2004.11243v1
• Date: Wed, 22 Apr 2020 10:17:24 GMT
• Title: Applications of shapelet transform to time series classification of earthquake, wind and wave data
• Authors: Monica Arul and Ahsan Kareem
• Abstract summary: "Shapelet transform" is based on local similarity in the shape of the time series subsequences. "White-box" machine learning model is proposed with understandable features and a transparent algorithm. Model automates event detection without the intervention of domain practitioners.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

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