Forecasting with Missing Data: Application to Coastal Wave Heights

This 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 signi®cant 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 notify the ̄eet. Several models are ®tted and compared by their forecasting behaviour. Copyright # 1999 John Wiley & Sons, Ltd.