Paper #213

Title:

Forecasting with missing data: Application to a real case

Authors:

Pedro Delicado and Ana Justel

Date:

May 1997

Abstract:

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.

Keywords:

Significant wave height, mean square error, linear interpolation, ARIMA models, nonparametric smoothing

JEL codes:

C22, C14

Area of Research:

Statistics, Econometrics and Quantitative Methods

Published in:

Journal of Forecasting, 18, 285-298, 1999
With the title:
Forecasting with missing data: Application to coastal wave heights

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