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Part of the book series: Advances in Soft Computing ((AINSC,volume 34))

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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 tech- niques 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.

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© 2006 Springer

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Juhos, I., Szarvas, G. (2006). Intelligent Forecast with Dimension Reduction. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_22

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  • DOI: https://doi.org/10.1007/3-540-31662-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31649-7

  • Online ISBN: 978-3-540-31662-6

  • eBook Packages: EngineeringEngineering (R0)

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