We develop a spatio-temporal model to forecast sensor output at five locations in North East England. The signal is described using coupled dynamic linear models, with spatial effects specified by a Gaussian process. Data streams are analysed using a stochastic algorithm, known as iterated batch importance sampling (IBIS), which sequentially propagates a discrete approximation of the parameter posterior through a series of reweighting and resampling steps. To circumvent degeneracy of the parameter samples, additional Markov chain Monte Carlo steps are used, subject to some degeneracy criterion. The IBIS algorithm is modified to make it more efficient and parallisable. The model is shown to give a good description of the underlying process and provide reasonable forecast accuracy.

我们开发了一个时空模型来预测英格兰东北部五个地点的传感器输出。使用耦合动态线性模型描述信号，并通过高斯过程指定空间效果。使用称为迭代批次重要性采样（IBIS）的随机算法分析数据流，该算法通过一系列重新加权和重采样步骤依次传播参数后验的离散近似值。为了避免参数样本的简并性，在遵循某些简并性准则的情况下，使用了附加的马尔可夫链蒙特卡罗步骤。修改了IBIS算法，使其更加有效和可并行化。该模型显示出对基础过程的良好描述，并提供了合理的预测准确性。