Hybrid ARIMA/RBF Framework for Prediction BUX Index

Dušan Marček

Research Institute of the IT4Innovations Centre of Excellence, The Silesian University in Opava, Bezruc Square 13, 74601 Opava, Czech Republic.

DOI: 10.4236/jcc.2015.35008                

Abstract

In this paper, we construct and implement a new architecture and learning method of customized hybrid RBF neural network for high frequency time series data forecasting. The hybridization is carried out using two running approaches. In the first one, the ARCH (Autoregressive Conditionally Heteroscedastic)-GARCH (Generalized ARCH) methodology is applied. The second modeling approach is based on RBF (Radial Basic Function) neural network using Gaussian activation function with cloud concept. The use of both methods is useful, because there is no knowledge about the relationship between the inputs into the system and its output. Both approaches are merged into one framework to predict the final forecast values. The question arises whether non-linear methods like neural networks can help modeling any non-linearities being inherent within the estimated statistical model. We also test the customized version of the RBF combined with the machine learning method based on SVM learning system. The proposed novel approach is applied to high frequency data of the BUX stock index time series. Our results show that the proposed approach achieves better forecast accuracy on the validation dataset than most available techniques.

Keywords

High Frequency Data, Statistical Forecasting Models, Rbf Neural Networks, Machine Learning

Conflicts of Interest

The authors declare no conflicts of interest.

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