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

arXiv: https://arxiv.org/abs/2004.11243 · HTML: https://mlsanity.com/paper/2004.11243/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology](https://mlsanity.com/paper/2301.12495/) (2023)
- [RocketPFN: Accurate Time Series Classification via In-Context Learning](https://mlsanity.com/paper/2606.21786/) (2026)
- [Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform](https://mlsanity.com/paper/2101.04516/) (2021)
- [AI-driven Java Performance Testing: Balancing Result Quality with Testing Time](https://mlsanity.com/paper/2408.05100/) (2024)
- [Multi-Granularity Framework for Unsupervised Representation Learning of Time Series](https://mlsanity.com/paper/2312.07248/) (2023)
- [Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets](https://mlsanity.com/paper/2409.08636/) (2024)
- [Statistical comparisons of time-series feature sets on classification tasks](https://mlsanity.com/paper/2608.01586/) (2026)

## Abstract (arXiv)

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.