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Improved prediction for a spatio-temporal model

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Abstract

We investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions using only the expectation given by the model. Further, we showed that these improvements in predictive accuracy were maintained even when using a reduced subset of the data for generating predictions.

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Acknowledgements

This research was partially supported under the Australian Research Council’s Discovery Projects funding scheme (project number DP180100836). We are grateful to Ichiro Ken Shimatani and two anonymous referees for their comments.

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Authors and Affiliations

  1. Research School of Finance, Actuarial Studies and Statistics, ANU College of Business and Economics, The Australian National University, Acton, ACT, 2601, Australia

    Gen Nowak & A. H. Welsh

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  1. Gen Nowak
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  2. A. H. Welsh
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Correspondence to Gen Nowak.

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Handling Editor: Bryan F. J. Manly.

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Nowak, G., Welsh, A.H. Improved prediction for a spatio-temporal model. Environ Ecol Stat 27, 631–648 (2020). https://doi.org/10.1007/s10651-020-00447-3

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  • DOI: https://doi.org/10.1007/s10651-020-00447-3

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