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Network Architectures for Prediction Perspective The architecture, or structure, of a predictor underpins its capacity to represent the dynamic properties of a statistically nonstationary discrete time input signal and hence its ability to predict or forecast some future value. This chapter therefore provides an overview of available structures for the prediction of discrete time signals. | Recurrent Neural Networks for Prediction Authored by Danilo P. Mandic Jonathon A. Chambers Copyright 2001 John Wiley Sons Ltd ISBNs 0-471-49517-4 Hardback 0-470-84535-X Electronic 3 Network Architectures for Prediction Perspective The architecture or structure of a predictor underpins its capacity to represent the dynamic properties of a statistically nonstationary discrete time input signal and hence its ability to predict or forecast some future value. This chapter therefore provides an overview of available structures for the prediction of discrete time signals. Introduction The basic building blocks of all discrete time predictors are adders delayers multipliers and for the nonlinear case zero-memory nonlinearities. The manner in which these elements are interconnected describes the architecture of a predictor. The foundations of linear predictors for statistically stationary signals are found in the work of Yule 1927 Kolmogorov 1941 and Wiener 1949 . The later studies of Box and Jenkins 1970 and Makhoul 1975 were built upon these fundamentals. Such linear structures are very well established in digital signal processing and are classified either as finite impulse response FIR or infinite impulse response IIR digital filters Oppenheim et al. 1999 . FIR filters are generally realised without feedback whereas IIR filters1 utilise feedback to limit the number of parameters necessary for their realisation. The presence of feedback implies that the consideration of stability underpins the design of IIR filters. In statistical signal modelling FIR filters are better known as moving average MA structures and IIR filters are named autoregressive AR or autoregressive moving average ARMA structures. The most straightforward version of nonlinear filter structures can easily be formulated by including a nonlinear operation in the output stage of an FIR or an IIR filter. These represent simple examples of nonlinear autoregressive NAR nonlinear moving average NMA or

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