Chaotic Time Series Prediction: Run for the Horizon
The present article reviews some recent papers concerned with chaotic time series prediction in the context of predictive clustering, and discusses in greater detail some novel techniques designed to avoid ‘a curse of exponential growth’ – errors grow exponentially depending on the number of steps ahead to be predicted. These techniques are non-successive observations, combined with a prognosis that employs already predicted values, the concept of non-predictable points, and a quality assessment of clusters used. The approach discussed, allows one to separate calculation into two parts: the first part, essentially larger, is performed off-line, the second, immediate prediction routine, is carried out on-line. This makes it possible to design fast and efficient prediction algorithms. A wide-ranging simulation, suggests that the error term associated with the prediction sub-model used, provided that clusters used to predict are chosen correctly, vanishes as the validation set size grows to infinity. Similarly, the error term associated with an incorrect choice of clusters used to predict, decreases when a validation set size increases.