A Method for Time Series Prediction using a Combination of Linear Models

This paper presents a new approach for time series prediction using local dynamic modeling. The proposed method is composed of three blocks: a Time Delay Line that transforms the original time series into a set of N −dimensional vectors, an Information-Theoretic based clustering method that segments the previous set into subspaces of similar vectors and a set of single layer neural networks that adjust a local model for each subspace created by the clustering stage. The results of this model are compared with those of another local modeling approach and of two representative global models in time series prediction: Tapped Delay Line Multilayer Perceptron (TDL-MLP) and Support Vector Regression (SVR).