Forecasting with missing data: application to coastal wave heights
Article Abstract:
A comparative analysis was conducted to evaluate linear and mixed econometric models to develop short-term forecasts for a real data series with a significantly large percentage of missing data. The data series involved a series of significant wave heights registered every three hours by a buoy in the Bay of Biscay. The final model proposed to forecast significant wave height in the short-term is a mixed model where the AR(16) was fitted to the linear part, free from then cyclic behavior of the series.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1999
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The usefulness of heuristic N(E)RLS alogorithms for combining forecasts
Article Abstract:
Computationally simple heuristic algorithms demonstrate similar performance with the optimizing algorithms in estimating Non-negativity Restricted Least Squares combinations models. In long fit data sets, heuristic algorithms produced an insignificant ex ante period proportion of 0.28% when compared with optimizing algorithms. In extreme short fit data sets, some heuristic algorithms produced significant efficiency losses.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1997
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