A hybrid system using multiple cyclic decomposition methods and neural network techniques for point forecast decision making

Data filtering methods are so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. In particular, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters without frequency information. We study the issues of integrated methods of joint time frequency analysis and neural network techniques as the application of multi-cyclic decomposition methods to the neural networks for short-term point forecast decision making The issues include the appropriate selection of neural network model architecture, for example, what type of neural network learning architecture is selected and what input size should be selected for our time series forecasting. We analyze these problems in particular with recurrent neural network learning and embedding dimension as chaos analysis. This study is also applied to a case study of daily Korean won/U.S. dollar exchange returns. Finally we suggest an integration framework for future research from our experimental results.