โ Scribed by Sameer Singh
No coin nor oath required. For personal study only.
Intelligent time-series forecasting is important in several applied domains. Arti"cially intelligent methods for forecasting are being consistently sought. The e!ect 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-"ltered and noise-injected time series from four di!erent sources: three benchmark series taken from the Santa Fe competition and the US "nancial 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.
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