A hierarchical spatiotemporal analog forecasting model for count data
Abstract 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 ecol...
Main Authors: | Patrick L. McDermott, Christopher K. Wikle, Joshua Millspaugh |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2018-01-01
|
Series: | Ecology and Evolution |
Subjects: | |
Online Access: | https://doi.org/10.1002/ece3.3621 |
Similar Items
-
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
by: Patrick L. McDermott, et al.
Published: (2019-02-01) -
Mortality and life expectancy forecast for (comparatively) high mortality countries
by: Ahbab Mohammad Fazle Rabbi, et al.
Published: (2018-11-01) -
Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method
by: Vasileios Evangelopoulos, et al.
Published: (2020-09-01) -
FORECASTING THE NUMBER OF UNEMPLOYED PEOPLE FROM ROMANIA USING HIERARCHICAL TIME SERIES
by: MARINOIU CRISTIAN
Published: (2016-08-01) -
Application of integrated Bayesian modeling and Markov chain Monte Carlo methods to the conservation of a harvested species
by: Fonnesbeck, C. J., et al.
Published: (2004-06-01)