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A Comparison of Box—Jenkins and objective methods for determining the order of a non‐seasonal ARMA Model
Author(s) -
Beveridge Steve,
Oickle Cyril
Publication year - 1994
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980130502
Subject(s) - box–jenkins , computer science , autoregressive–moving average model , series (stratigraphy) , model selection , selection (genetic algorithm) , data mining , time series , econometrics , machine learning , order (exchange) , artificial intelligence , autoregressive model , autoregressive integrated moving average , mathematics , paleontology , biology , finance , economics
Abstract This paper evaluates different procedures for selecting the order of a non‐seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box‐Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time‐consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time‐series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.