Abstract
In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neural Network (NN) methods, Lower Upper Bound Estimation (LUBE) and Multi-objective Evolutionary Algorithm (MOEA) LUBE (MLUBE), for multi-step PI. Furthermore, we propose two new MOEA methods based on a 2-phase gradient and MOEA based learning: M2LUBET1 and M2LUBET2. Also, we present a robust evaluation procedure to compare PI methods. Using four distinct seasonal time series, we compared all four PI methods. Overall, competitive results were achieved by the 2-phase learning methods, in terms of both predictive performance and computational effort.
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Acknowledgments
This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/-CEC/-00319/-2013, and project: NORTE-01-0247-FEDER-017497.
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Pereira, P.J., Cortez, P., Mendes, R. (2017). Multi-objective Learning of Neural Network Time Series Prediction Intervals. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_46
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DOI: https://doi.org/10.1007/978-3-319-65340-2_46
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