A nonlinear model for time series prediction and signal interpolation

The approach is an extension of the method of radial basis functions. Parameter estimation for the nonlinear predictor is performed by a gradient descent over a mean squared error measure, starting from a random initialization of the parameters. Results on predicting segments of speech data and the sunspot series are presented and compared to a linear predictor. An approach to adaptive estimation of the model by means of an extended Kalman filter is presented. In terms of prediction residual, the nonlinear predictor is found to perform significantly better than a linear model with the same number of parameters. Difficulties in applying this model in speech processing are discussed