TY - JOUR
T1 - A hierarchical spatiotemporal analog forecasting model for count data
AU - McDermott, Patrick L.
AU - Wikle, Christopher K.
AU - Millspaugh, Joshua
N1 - Publisher Copyright:
© 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
KW - ecological forecasting
KW - hierarchical Bayesian models
KW - nonlinear forecasting
KW - waterfowl settling patterns
UR - http://www.scopus.com/inward/record.url?scp=85043477484&partnerID=8YFLogxK
U2 - 10.1002/ece3.3621
DO - 10.1002/ece3.3621
M3 - Article
AN - SCOPUS:85043477484
SN - 2045-7758
VL - 8
SP - 790
EP - 800
JO - Ecology and Evolution
JF - Ecology and Evolution
IS - 1
ER -