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Forecasting, Environmental

Stochastic Modeling and Environmental Change

  1. Peter C. Young1,2,
  2. Diego J. Pedregal3

Published Online: 15 SEP 2006

DOI: 10.1002/9780470057339.vaf007

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Young, P. C. and Pedregal, D. J. 2006. Forecasting, Environmental. Encyclopedia of Environmetrics. 3.

Author Information

  1. 1

    Lancaster University, UK

  2. 2

    Australian National University College of Medicine, Biology & Environment, Canberra, Australia

  3. 3

    Universidad de Castilla la Mancha, spain

Publication History

  1. Published Online: 15 SEP 2006


The term ‘environmental forecasting’ is open to a range of interpretations, from ‘black-box’ time series analysis and forecasting, based on relatively simple stochastic methods; to ‘mechanistic’ prediction based on ‘physically meaningful’, and normally quite complex, deterministic simulation models. This article, however, concentrates on stochastic forecasting methods based on models that range from purely black-box time series characterizations of data to data-based mechanistic (DBM) models that are physically meaningful but, at the same time, fairly simple in their structure and signal topology. There are numerous statistical approaches to forecasting, from simple, regression-based methods to optimal statistical procedures formulated in stochastic state–space terms. Since it would be impossible to review all these methods here, this article first reviews briefly those that are felt to have most significance in theoretical and practical terms within the specific context of environmental forecasting. It then discusses in more detail a unified approach to forecasting based on a generic unobserved component (UC) model, formulated in stochastic, state–space terms.