Dynamic time-series forecasting using local approximation

Pattern recognition techniques for time-series forecasting are beginning to be realised as an important tool for predicting chaotic behaviour of dynamic systems. In this paper we develop the concept of a pattern modelling and recognition system which is used for predicting future behaviour of time-series using local approximation. In this paper we compare this forecasting tool with neural networks. We also study the effect of noise filtering on the performance of the proposed system. Fourier analysis is used for noise-filtering the time-series. The results show that Fourier analysis is an important tool for improving the performance of the proposed forecasting system. The results are discussed on three benchmark series and the real US S&P financial index