Noise impact on time-series forecasting using an intelligent pattern matching technique

Intelligent time-series forecasting is important in several applied domains. Artificially intelligent methods for forecasting are being consistently sought. The effect of noise on time-series prediction is important to quantify for accurate forecasting with these systems. Conventionally, noise is considered obstructive to accurate forecasting. In this paper we analyse the noise impact on time-series forecasting using a pattern recognition technique for one-step ahead forecasting called the “Pattern Modelling and Recognition System”. We evaluate the system performance on noise-filtered and noise-injected time-series from four different sources: three benchmark series taken from the Santa Fe competition and the US financial index, S&P series. The results are discussed when comparing the proposed method against the established Exponential smoothing method and Neural networks and some important conclusions drawn on their basis.