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Optimal choice of type and order of river flow time series models
Author(s) -
Rao A. Ramachandra,
Kashyap R. L.,
Mao LiangTsi
Publication year - 1982
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr018i004p01097
Subject(s) - autoregressive model , bayes' theorem , mathematics , transformation (genetics) , series (stratigraphy) , star model , autoregressive–moving average model , decision rule , statistics , type (biology) , moving average , time series , econometrics , autoregressive integrated moving average , bayesian probability , geology , paleontology , biochemistry , chemistry , gene
The performance of various types of models for river flows are compared by using a decision rule derived from the Bayes criterion. The decision rule has the property that it minimizes the probability of error. The best model among the autoregressive, autoregressive moving average, and moving average models of various orders for the annual flows of about 10 rivers is found by using this decision rule. For the monthly flows, not only the best seasonal autoregressive integrated moving average model but also the best type of transformation is determined. The models for the log transformed monthly data are superior to the models fitted to the observed data without transformation. The variability in the decisions caused by the different prior probability density functions is also discussed.