Delicado, PedroJustel, AnaUniversitat Pompeu Fabra. Departament d'Economia i Empresa2017-07-262017-07-261997-05-01Journal of Forecasting, 18, 285-298, 1999http://hdl.handle.net/10230/795This paper presents a comparative analysis of linear and mixed models for short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay. The series is interpolated with a linear predictor which minimizes the forecast mean square error. The linear models are seasonal ARIMA models and the mixed models have a linear component and a non linear seasonal component. The non linear component is estimated by a non parametric regression of data versus time. Short term forecasts, no more than two days ahead, are of interest because they can be used by the port authorities to notice the fleet. Several models are fitted and compared by their forecasting behavior.application/pdfengL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commonsinfo:eu-repo/semantics/workingPapersignificant wave heightmean square errorlinear interpolationarima modelsnonparametric smoothingStatistics, Econometrics and Quantitative Methodsinfo:eu-repo/semantics/openAccess