A coral reef optimization algorithm for wave height time series segmentation problems

Áreas de investigación:

• Methodology - Machine learning

Año:

2017

Tipo de publicación:

Artículo en conferencia

Autores:

• Durán-Rosal, Antonio Manuel
• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Salcedo-Sanz, Sancho
• Hervás-Martínez, César

Volumen:

10305

Título del libro:

14th International Work-Conference on Artificial and Natural Neural Networks (IWANN2017)

Serie:

Lecture Notes in Computer Science (LNCS)

Páginas:

673-684

Organización:

Cádiz (Spain)

Mes:

14th-16th June

ISBN:

978-3-319-59152-0

BibTex:

@conference{Duran-Rosal2017, author = "Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time series segmentation can be approached using metaheuristics procedures such as genetic algorithms (GAs) methods, with the purpose of automatically finding segments and determine similarities in the time series with the lowest possible clustering error. In this way, segments belonging to the same cluster must have similar properties, and the dissimilarity between segments of different clusters should be the highest possible. In this paper we tackle a specific problem of significant wave height time series segmentation, with application in coastal and ocean engineering. The basic idea in this case is that similarity between segments can be used to characterise those segments with high significant wave heights, and then being able to predict them. A recently metaheuristic, the Coral Reef Optimization (CRO) algorithm is proposed for this task, and we analyze its performance by comparing it with that of a GA in three wave height time series collected in three real buoys (two of them in the Gulf of Alaska and another one in Puerto Rico). The results show that the CRO performance is better than the GA in this problem of time series segmentation, due to the better exploration of the search space obtained with the CRO.", booktitle = "14th International Work-Conference on Artificial and Natural Neural Networks (IWANN2017)", doi = "10.1007/978-3-319-59153-7_58", isbn = "978-3-319-59152-0", month = "14th-16th June", organization = "C{\'a}diz (Spain)", pages = "673-684", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{A} coral reef optimization algorithm for wave height time series segmentation problems ", url = "dx.doi.org/10.1007/978-3-319-59153-7_58", volume = "10305", year = "2017", }

Abstract:

Time series segmentation can be approached using metaheuristics procedures such as genetic algorithms (GAs) methods, with the purpose of automatically finding segments and determine similarities in the time series with the lowest possible clustering error. In this way, segments belonging to the same cluster must have similar properties, and the dissimilarity between segments of different clusters should be the highest possible. In this paper we tackle a specific problem of significant wave height time series segmentation, with application in coastal and ocean engineering. The basic idea in this case is that similarity between segments can be used to characterise those segments with high significant wave heights, and then being able to predict them. A recently metaheuristic, the Coral Reef Optimization (CRO) algorithm is proposed for this task, and we analyze its performance by comparing it with that of a GA in three wave height time series collected in three real buoys (two of them in the Gulf of Alaska and another one in Puerto Rico). The results show that the CRO performance is better than the GA in this problem of time series segmentation, due to the better exploration of the search space obtained with the CRO.