Self-Organizing Maps
Compressed random-access trees for spatially coherent data
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
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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.