Measuring Time Series Predictability Using Support Vector Regression |
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Authors: | João R. Sato Sergi Costafreda Pedro A. Morettin Michael John Brammer |
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Affiliation: | 1. Institute of Mathematics and Statistics, University of S?o Paulo , S?o Paulo, Brazil;2. LIM44/NIF, Institute of Radiology, University of S?o Paulo , S?o Paulo, Brazil jrsatobr@gmail.com;4. Brain Image Analysis Unit, Institute of Psychiatry , King's College , London, UK;5. Institute of Mathematics and Statistics, University of S?o Paulo , S?o Paulo, Brazil |
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Abstract: | Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient. |
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Keywords: | Autoregressive Machine learning Non-linear Prediction Regression Support vector |
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