Forecasting by volterra type models

Abstract

An inverse Volterra type model for fitting time series data is investigated here. Based on polyspectral analysis of the process an identification and estimation technique is developed, the algorithm for which is presented here. The methodology has been applied to two well-known nonlinear processes viz., the Canadian Lynx Series and the Wolfer's Sunspot Series. A one step ahead predictor is constructed, forecast results show considerable improvement over linear fitting and in some situations may be better than those obtained from bilinear modelling.

Keywords:

• Cross Spectrum
• Bispectrum
• Transfer Function
• AIC