Noisy Time-Series Prediction using Pattern Recognition Techniques

Time-series prediction is important in physical and financial domains. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This paper describes a Pattern Modelling and Recognition System (PMRS) which is used for forecasting benchmark series and the US S&P financial index. The main aim of this paper is to evaluate the performance of such a system on noise free and Gaussian additive noise injected time-series. The results show that the addition of Gaussian noise leads to better forecasts. The results also show that the Gaussian noise standard deviation has an important effect on the PMRS performance. PMRS results are compared with the popular Exponential Smoothing method.