A hierarchical spatiotemporal analog forecasting model for count data

Patrick L. McDermott, Christopher K. Wikle, Joshua Millspaugh

    Research output: Contribution to journalArticlepeer-review


    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 applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model-based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.

    Original languageEnglish
    Pages (from-to)790-800
    Number of pages11
    JournalEcology and Evolution
    Issue number1
    StatePublished - Jan 2018


    • ecological forecasting
    • hierarchical Bayesian models
    • nonlinear forecasting
    • waterfowl settling patterns


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