Local averaging optimization for chaotic time series prediction

Local models have emerged as one of the most accurate methods of time series prediction, but their performance is sensitive to the choice of user-specified parameters such as the size of the neighborhood, the embedding dimension, and the distance metric. This paper describes a new method of optimizing these parameters to minimize the multi-step cross-validation error. Empirical results indicate that multi-step optimization is susceptible to shallow local minima unless the optimization is limited to ten or fewer steps ahead. The models optimized using the new method consistently performed better than those optimized with adaptive analog forecasts.