In this paper, we investigate two general methods of modeling and prediction, the orthogonal search method and canonical variate analysis approach, to time-series data. Nonlinear autoregressive moving average (ARMA) and state affine models are adopted for approximation and developed as one step predictors. An unknown nonlinear time-invariant system is assumed to have the Markov property of finite order so that the one step predictors are finite dimensional. No special assumptions are made about the model terms, model order or state dimensions. Computer simulations are presented for Lorenz attractor.
|Number of pages
|Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
|Published - 1995
|Proceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) - Montreal, Can
Duration: 20 Sep 1995 → 23 Sep 1995