Clavería González, ÓscarMonte Moreno, EnricTorra Porras, Salvador2020-12-112020-12-1120171535-6698https://hdl.handle.net/2445/172665Machine 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.21 p.application/pdfeng(c) Nova Science Publishers, 2017Aprenentatge automàticDistribució de GaussAnàlisi de regressióXarxes neuronals convolucionalsMachine learningGaussian distributionRegression analysisConvolutional neural networksinfo:eu-repo/semantics/article7050632020-12-11info:eu-repo/semantics/openAccess