Abstract: In this paper, an adaptive model is documented for predicting financial time series integrating signal processing, information fusion and computational intelligence. The model consists of financial time series (FTS)-specific Empirical Mode Decomposition (EMD) for signal processing, Principal Component Analysis (PCA) for dimension reduction, and Artificial Neural Networks (ANN) for nonlinear prediction. The model uses a sliding window to capture the most recent time series data, applies EMD to transform the data into multilevel Intrinsic Mode Functions (IMF's). PCA is then used to reduce the dimension of IMF's and to generate a set of information-rich features which are input into an ANN to generate the output as prediction. This novel model of prediction implements an information fusion process consisting of signal decomposition, dimension reduction and nonlinear synthesis. This model lifts the prediction capability to a new level. The originality of this work exhibits in fouraspects:1) a structural reformulation of EMD algorithm, providing an interface to more information fusion, 2)deepening into finer time frames for tackling the end effect of EMD andimplementation and testing on two levels of time frameimplementation,3) investigation of multi-step prediction, 4) a generic framework of prediction models for financial time series with upgradability and extensibility. The use of sliding window for EMD also gets the test closer to the reality.The new model is tested on the historical data of two stock indices-Chinese HS300 and Australian AORD, the performance, achieving a hit rate of 78% and 82% on HS300 D1 and M15, and 74% on AXJO D1 respectively, significantly higher than 5 existing models after comparison.

Key words: financial time series prediction, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Principal Component Analysis (PCA), Artificial Neural Networks (ANN)