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 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 ...
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 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.
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