Forecasting with missing data: application to coastal wave heights

✍ Scribed by Pedro Delicado; Ana Justel

• Book ID: 101285862
• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 238 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(199907)18:4<285::aid-for714>3.0.co;2-e

✦ Synopsis

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.