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Abstract:
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Analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes. In essence, analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous work on analog forecasting has typically been presented in an empirical or heuristic context, as opposed to a formal statistical framework. We propose a Bayesian model for analog forecasting, building upon previous analog methods. Thus, unlike traditional analog forecasting methods, the use of Bayesian modeling allows one to rigorously quantify uncertainty to obtain realistic posterior predictive forecasts. The model is applied to the long-lead time forecasting of mid-May averaged soil moisture anomalies in Iowa over a high-resolution grid of spatial locations. We also further develop the model in a hierarchical framework for the purpose of forecasting count-valued data by using nonnegative matrix factorization (NMF) to conduct dimension reduction. This extension of the model is applied to the forecasting of waterfowl counts in the United States and Canada.
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