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