Classical nonlinear models for time series prediction exhibit improved capabilities compared to linear ones. Nonlinear regression has however drawbacks, such as overfitting and local minima problems, user-adjusted parameters, higher computation times, etc. There is thus a need for simple nonlinear models with a restricted number of learning parameters, high performances and reasonable complexity. In this paper, we present a method for nonlinear forecasting based on the quantization of vectors concatenating inputs (regressors) and outputs (predictions). Weighting techniques are applied to give more importance to inputs and outputs respectively. The method is illustrated on standard time series prediction benchmarks.

Nonlinear time-series prediction offers potential performance increases compared to linear models. Nevertheless, the enhanced complexity and computation time often prohibits an efficient use of nonlinear tools. In this paper, we present a simple nonlinear procedure for time-series forecasting, based on the use of vector quantization techniques; the values to predict are considered as missing data, and the vector quantization methods are shown to be compatible with such missing data. This method offers an alternative to more complex prediction tools, while maintaining reasonable complexity and computation time.