Codebook prediction: a nonlinear signal modeling paradigm

A nonlinear generalization of the family of autoregressive signal models is introduced. This generalization can be viewed as an autoregressive model with state-varying parameters. For such signals, minimum mean-square error prediction can be reformulated as an interpolation problem. A novel interpretation of the signal as a codebook for its own prediction leads to an interpolation strategy resembling a predictive counterpart to vector quantization. The applicability of this model is then demonstrated empirically for a variety of signals.<<ETX>>

[1]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[2]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[3]  S. Yakowitz.  NEAREST‐NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS , 1987 .

[4]  Farmer,et al.  Predicting chaotic time series. , 1987, Physical review letters.

[5]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[6]  K. P. Unnikrishnan,et al.  Nonlinear prediction of speech signals using memory neuron networks , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.