Identification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation

Research areas:

• Methodology - Machine learning

Year:

2017

Type of Publication:

Article

Authors:

• Durán-Rosal, Antonio Manuel
• Dorado-Moreno, Manuel
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Progress in Artificial Intelligence

Volume:

6

Number:

1

Pages:

59-66

Month:

March

ISSN:

2192-6360

BibTex:

@article{182016, author = "Antonio Manuel Dur{\'a}n-Rosal and Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents four configurations of a genetic algorithm (GA) combined with a local search (LS) method for time series segmentation with the purpose of correctly recognising extreme values. The LS method is based on likelihood maximisation of a beta distribution. The proposal is tested on three real ocean wave height time series, where extreme values are frequently found. Concretely, the time series are taken from two oceanographic buoys in the Gulf of Alaska, and another one from Puerto Rico. The results show that the different combinations of LS improve the results of the GA. Furthermore, the algorithm provides segmentations where extreme values are grouped in a well defined cluster, which allows the study of the characteristics of this type of events.", doi = "10.1007/s13748-016-0105-1", issn = "2192-6360", journal = "Progress in Artificial Intelligence", month = "March", number = "1", pages = "59-66", title = "{I}dentification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation", url = "http://dx.doi.org/10.1007/s13748-016-0105-1", volume = "6", year = "2017", }

Abstract:

This paper presents four configurations of a genetic algorithm (GA) combined with a local search (LS) method for time series segmentation with the purpose of correctly recognising extreme values. The LS method is based on likelihood maximisation of a beta distribution. The proposal is tested on three real ocean wave height time series, where extreme values are frequently found. Concretely, the time series are taken from two oceanographic buoys in the Gulf of Alaska, and another one from Puerto Rico. The results show that the different combinations of LS improve the results of the GA. Furthermore, the algorithm provides segmentations where extreme values are grouped in a well defined cluster, which allows the study of the characteristics of this type of events.