Modeling and prediction of time-series data by orthogonal search and canonical variate analysis

Yu Te Wu*, Mingui Sun, Robert J. Sclabassi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

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