Detecting stationary intervals for random waves using time series clustering

Carolina Euán, Joaquín Ortega, Pedro C. Alvarez-Esteban

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The problem of detecting changes in the state of the sea is very important for the analysis and determination of wave climate in a given location. Wave measurements are frequently statistically analyzed as a time series, and segmentation algorithms developed in this context are used to determine change-points. However, most methods found in the literature consider the case of instantaneous changes in the time series, which is not usually the case for sea waves, where changes take a certain time interval to occur. We propose a new segmentation method that allows for the presence of transition intervals between successive stationary periods, and is based on the analysis of distances of normalized spectra to detect clusters in the time series. The series is divided into 30-minutes intervals and the spectral density is estimated for each one. The normalized spectra are compared using the Total Variation distance and a hierarchical clustering method is applied to the distance matrix. The information obtained from the clustering algorithm is used to classify the intervals as belonging to a stationary or a transition period We present simulation studies to validate the method and examples of applications to real data.
Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
PublisherAmerican Society of Mechanical Engineers (ASME)infocentral@asme.org
ISBN (Print)9780791845431
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

Fingerprint Dive into the research topics of 'Detecting stationary intervals for random waves using time series clustering'. Together they form a unique fingerprint.

Cite this