Clavería González, ÓscarMonte Moreno, EnricTorra Porras, Salvador2017-11-142017-11-142017https://hdl.handle.net/2445/117730Machine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model to that of different neural network architectures in a multi-step-ahead time series prediction experiment. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.22 p.application/pdfeng(c) Nova Science Publishers, Inc., 2017Aprenentatge automàticDistribució de GaussAnàlisi de regressióPrevisióMachine learningGaussian distributionRegression analysisForecastinginfo:eu-repo/semantics/bookPart304795info:eu-repo/semantics/openAccess