Sequential Bayesian inference for spatio-temporal models of temperature and humidity data
Abstract
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 which sequentially approximates the parameter posterior through a series of reweighting and resampling steps. An iterated batch importance sampling scheme is used to circumvent particle degeneracy through a resample-move step. The 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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2018
- DOI:
- 10.48550/arXiv.1806.05424
- arXiv:
- arXiv:1806.05424
- Bibcode:
- 2018arXiv180605424L
- Keywords:
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- Statistics - Applications;
- Statistics - Computation
- E-Print:
- 25 pages