The Application of Modern Optimization Algorithm in Time Series Prediction
- https://doi.org/10.2991/icadme-15.2015.62How to use a DOI?
- Stochastic chaos (SC), Principal components analysis (PCA), Stochastic volatility jump diffusion (SVJD), Artificial neural network (ANN), Genetic algorithm (GA).
This paper applies the SC model and SVJD model to artificially generated data, and we put forward a forecast model that hybridizes genetic algorithm, principal component analysis and artificial neural network methods. This article utilizing genetic algorithm to search for the initial weights of the BP neural network could guarantee a relatively high probability to obtain the global optima, and we include principle component analysis (PCA) to extract contribution rate to meet 85% of the principal component as the input variables. The experiment results demonstrate that the combination methods PCA-BP and PCA-GA-BP model is adopted to overcome the fitting compared with the traditional forecasting method.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhaoyue Hu AU - Yanping Bai PY - 2015/10 DA - 2015/10 TI - The Application of Modern Optimization Algorithm in Time Series Prediction BT - Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering PB - Atlantis Press SP - 312 EP - 315 SN - 2352-5401 UR - https://doi.org/10.2991/icadme-15.2015.62 DO - https://doi.org/10.2991/icadme-15.2015.62 ID - Hu2015/10 ER -