Heterogeneous local model networks for time series prediction

• Published in: Applied mathematics and computation Vol. 168; no. 1; pp. 164 - 177
• Main Authors: Oh, Sang-Keon, Kim, Min-Soeng, Eom, Tae-Dok, Lee, Ju-Jang
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.09.2005; Elsevier
• Subjects: Cross-validation; Exact sciences and technology; Inference from stochastic processes; time series analysis; Local modeling; Mathematics; Mixture of experts network; Multivariate analysis; PCA; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications); Statistics; Time series prediction; Local modeling; Time series prediction; Mixture of experts network; Cross-validation; PCA; Applied mathematics; Time series; Prediction theory; Cross validation; Expert network; Divide and conquer method; Principal component analysis
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2004.08.018

The approaches of local modeling have emerged as one of the promising methods of time series prediction. By use of the divide-and-conquer method, local models can exploit state-dependent features to approximate a subset of training data accurately. However, the generalization performance of local model networks is subject to the proper selection of model parameters. In this paper, we present a new method for local model construction for the noisy time series prediction. The proposed method uses the principal component analysis (PCA) and cross-validation technique to construct an optimal input vector for each local model. A heuristic learning rule is also proposed to update the mixture of experts network structure, which determines the confidence level of local prediction model. The proposed method has been tested with noisy Mackey–Glass time series and Sunspot series.