Financial Forecasting Using Pattern Modeling and Recognition System Based on Kernel Regression

The increased popularity of financial time series forecasting in recent times lies to its great importance in predicting the best stock market timing. In this paper, we develop the concept of a pattern modeling and recognition system for predicting future behavior of time series using local approximation. In order to improve the performance of this system, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and use this method for filtering the noise of the time series. The computational results on the well-known stock market indices reveal that kernel regression is an important tool for improving the performance of the proposed forecasting system, and the performance of the improved method can outperform the performance of advanced methods such as neural networks. Key-Words: financial time series; forecasting; pattern modeling and recognition system; kernel regression