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.

中文翻译：

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改进的时空模型预测

我们调查了一个框架，用于改进时空数据模型的预测。该框架基于最小化均方预测误差，可应用于许多模型。我们将该框架应用于澳大利亚Murray-Darling盆地的每月降雨数据模型。在各种预测情况下，与仅使用模型给出的预期相比，我们提高了预测准确性。此外，我们表明，即使使用减少的数据子集来生成预测，仍可以保持这些预测准确性的提高。