Abstract
The amount of data available in time series is recently increasing in an exponential way, making difficult time series preprocessing and analysis. This paper adapts different methods for time series representation, which are based on time series segmentation. Specifically, we consider a particle swarm optimization algorithm (PSO) and its barebones exploitation version (BBePSO). Moreover, a new variant of the BBePSO algorithm is proposed, which takes into account the positions of the particles throughout the generations, where those close in time are given more importance. This methodology is referred to as weighted BBePSO (WBBePSO). The solutions obtained by all the algorithms are finally hybridised with a local search algorithm, combining simple segmentation strategies (Top-Down and Bottom-Up). WBBePSO is tested in 13 time series and compared against the rest of algorithms, showing that it leads to the best results and obtains consistent representations.
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Acknowledgement
This work has been subsidized by the projects TIN2017-85887-C2-1-P, TIN2014-54583-C2-1-R and TIN2015-70308-REDT of the Spanish Ministry of Economy and Competitiveness (MINECO), and FEDER funds (FEDER EU). The research of Antonio M. Durán-Rosal and David Guijo-Rubio have been subsidized by the FPU Predoctoral Program of the Spanish Ministry of Education, Culture and Sport (MECD), grant references FPU14/03039 and FPU16/02128.
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Durán-Rosal, A.M., Guijo-Rubio, D., Gutiérrez, P.A., Hervás-Martínez, C. (2018). Hybrid Weighted Barebones Exploiting Particle Swarm Optimization Algorithm for Time Series Representation. In: Korošec, P., Melab, N., Talbi, EG. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture Notes in Computer Science(), vol 10835. Springer, Cham. https://doi.org/10.1007/978-3-319-91641-5_11
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