An Innovation State Space Approach for Time Series Forecasting

An innovation state space modelling approach is presented in which the structures and parameters of a model are determined by an identification algorithm proposed by Tse and Weinert ( IEEE Trans. Automat. Contr. 120 (1975), 603–13) and the singular value decomposition technique. This approach is applied to two typical data series to illustrate its use, and its forecasting accuracy is compared with other time series approaches.