Intelligent forecast with dimension reduction

Abstract

Time-series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, Artificial Neural Networks and Support Vector Machines, are used to provide accurate non-linear models of the problem. In spite of the effectiveness of these methods we have to solve two problems. Firstly, time-series can have noise and a high dimensional embedding space. Secondly, the learning depends on several hyper-parameters that need to be set properly. To handle these problems we apply noise and dimension reduction techniques and model selection to get suitable hyper-parameters. Then, we introduce a meta-heuristic to refine the predictions of the selected models. Our experiments show improvements in the quality of predictions of a real-life problem compared to two 'benchmark' algorithms. © 2006 Springer.