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Activity Number: 322 - ENVR Student Paper Award Winners
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #322919 View Presentation
Title: A Hierarchical Spatio-Temporal Analog Forecasting Model for Nonlinear Ecological Processes
Author(s): Patrick McDermott* and Christopher Wikle and Joshua Millspaugh
Companies: University of Missouri and University of Missouri and University of Montana
Keywords: Nonlinear forecasting ; hierarchical Bayesian models ; ecological forecasting ; dynamical system ; nonnegative matrix factorization

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.

Authors who are presenting talks have a * after their name.

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