MLP, Gaussian Processes and Negative Correlation Learning for Time Series Prediction

Authors:
Waleed M. Azmy;Neamat Gayar;Amir F. Atiya;Hisham El-Shishiny
Affiliations:
Faculty of Computers and Information, Cairo University, Giza, Egypt 12613;Faculty of Computers and Information, Cairo University, Giza, Egypt 12613 and Center of Informatics Science, Nile University, Giza, Egypt;Faculty of Engineering, Cairo University, Giza, Egypt;IBM Center of Advanced Studies in Cairo, IBM Cairo Technology Development Center, Giza, Egypt
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

Time series forecasting is a challenging problem, that has a wide variety of application domains such as in engineering, environment, finance and others. When confronted with a time series forecasting application, typically a number of different forecasting models are tested and the best one is considered. Alternatively, instead of choosing the single best method, a wiser action could be to choose a group of the best models and then to combine their forecasts. In this study we propose a combined model consisting of Multi-layer perceptron (MLP), Gaussian Processes Regression (GPR) and a Negative Correlation Learning (NCL) model. The MLP and the GPR were the top performers in a previous large scale comparative study. On the other hand, NCL suggests an alternative way for building accurate and diverse ensembles. No studies have reported on the performance of the NCL in time series prediction. In this work we test the efficiency of NCL in predicting time series data. Results on two real data sets show that the NCL is a good candidate model for forecasting time series. In addition, the study also shows that the combined MLP/GPR/NCL model outperforms all models under consideration.