A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation
Autor
Durán Rosal, Antonio Manuel
Gutiérrez-Peña, Pedro Antonio
Carmona Poyato, Ángel
Hervás-Martínez, César
Editor
ElsevierFecha
2019Materia
Time series size reductionTime series segmentation
Particle swarm optimisation
Hybrid algorithm
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This paper proposes new methods based on time series segmentation, including the adaptation of the particle swarm optimisation algorithm (PSO) to this problem, and more advanced PSO versions, such as barebones PSO (BBPSO) and its exploitation version (BBePSO). Moreover, a novel algorithm is derived, referred to as dynamic exploitation barebones PSO (DBBePSO), which updates the importance of the social and cognitive components throughout the generations. All these algorithms are further improved by considering a final local search step based on the combination of two well-known standard segmentation algorithms (Bottom-Up and Top-Down). The performance of the different methods is evaluated using 15 time series from various application fields, and the results show that the novel algorithm (DBBePSO) and its hybrid version (HDBBePSO) outperform the rest of segmentation techniques.