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A model‐based approach for analog spatio‐temporal dynamic forecasting
Author(s) -
McDermott Patrick L.,
Wikle Christopher K.
Publication year - 2016
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2374
Subject(s) - bayesian probability , computer science , variety (cybernetics) , probabilistic forecasting , nonlinear system , bayesian inference , grid , econometrics , mathematics , probabilistic logic , artificial intelligence , physics , geometry , quantum mechanics
Analog forecasting has been applied in a variety of fields for predicting future states of complex nonlinear systems that require flexible forecasting methods. Past analog methods have almost exclusively been used in an empirical framework without the structure of a model‐based approach. We propose a Bayesian model framework for analog forecasting, building upon previous analog methods but accounting for parameter uncertainty. Thus, unlike traditional analog forecasting methods, the use of Bayesian modeling allows one to rigorously quantify uncertainty to obtain realistic posterior predictive distributions. 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. Sea surface temperature is used to find past time periods with similar trajectories to the current pre‐forecast period. The analog model is developed on projection coefficients from a basis expansion of the soil moisture and sea surface temperature fields. Separate models are constructed for locations falling in each Iowa Crop Reporting District, and the forecasting ability of the proposed model is compared against a variety of alternative methods and metrics. Copyright © 2015 John Wiley & Sons, Ltd.