Financial Time Series Forecasting Using Hybrid Wavelet-Neural Model

In this paper, we examine and discuss results of financial time series prediction by using a combination of wavelet transform, neural networks and statistical time series analytical techniques. The analyzed hybrid model combines the capabilities of wavelet packet transform and neural networks that can capture hidden but crucial structure attributes embedded in the time series. The input data is decomposed into a wavelet representation using two different resolution levels. For each of the new time series, a neural network is created, trained and used for prediction. In order to create an aggregate forecast, the individual predictions are combined with statistical features extracted from the original input. Additional to the conclusion that the increase in resolution level does not improve the prediction accuracy, the analysis of obtained results indicates that the suggested model presents satisfactory predictor. The results also serve as an indication that denoising process generates more accurate results when applied.