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Springerplus. 2015 Jan 13;4:12. doi: 10.1186/s40064-014-0774-9. eCollection 2015.

On multivariate imputation and forecasting of decadal wind speed missing data.

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1
School of Statistics and Planning, Makerere University, P.O. Box 7062, Kampala, Uganda.

Abstract

This paper demonstrates the application of multiple imputations by chained equations and time series forecasting of wind speed data. The study was motivated by the high prevalence of missing wind speed historic data. Findings based on the fully conditional specification under multiple imputations by chained equations, provided reliable wind speed missing data imputations. Further, the forecasting model shows, the smoothing parameter, alpha (0.014) close to zero, confirming that recent past observations are more suitable for use to forecast wind speeds. The maximum decadal wind speed for Entebbe International Airport was estimated to be 17.6 metres per second at a 0.05 level of significance with a bound on the error of estimation of 10.8 metres per second. The large bound on the error of estimations confirms the dynamic tendencies of wind speed at the airport under study.

KEYWORDS:

Forecasting; Imputations; Missing data; Statistical models; Wind speed

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