The Application of Modern Optimization Algorithm in Time Series Prediction

DOI

10.2991/icadme-15.2015.62How to use a DOI?

Keywords

Stochastic chaos (SC), Principal components analysis (PCA), Stochastic volatility jump diffusion (SVJD), Artificial neural network (ANN), Genetic algorithm (GA).

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/icadme-15.2015.62
ID  - Hu2015/10
ER  -