Abstract
A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and implement the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to electricity consumption from a public Spanish university, by using a Spark cluster.
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Acknowledgments
The authors would like to thank the Spanish Ministry of Economy and Competitiveness, Junta de Andalucía, Fundación Pública Andaluza Centro de Estudios Andaluces and Universidad Pablo de Olavide for the support under projects TIN2014-55894-C2-R, P12-TIC-1728, PRY153/14 and APPB813097, respectively.
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Talavera-Llames, R.L., Pérez-Chacón, R., Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F. (2016). A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_15
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DOI: https://doi.org/10.1007/978-3-319-32034-2_15
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