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Automatic arima modelling by the cartesian search algorithm
Author(s) -
Höglund Rune,
Östermark Ralf
Publication year - 1991
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.3980100503
Subject(s) - autoregressive integrated moving average , univariate , algorithm , cartesian coordinate system , set (abstract data type) , computer science , data set , series (stratigraphy) , inverse , statistics , time series , mathematics , multivariate statistics , paleontology , geometry , biology , programming language
Abstract In the present study we report on the development and test results of a Cartesian ARIMA Search Algorithm, designed for automatic generation of univariate models for time series data within specified parameter intervals of the identification and estimation stages. Model retention is determined within a preselected set of statistics. By interpreting these statistics as dimensions of the constructed criterion space, we obtain a subset of non‐dominated models according to the rule of maximum dispersion over the efficient set. The CARIMA algorithm allows free specification of the number of criteria used in the runs. The algorithm was tested with both simulated and real economic data. The results based on simulated data indicate that the precision of the CARIMA algorithm is lower for seasonal models and higher for non‐seasonal ones, thus suggesting an inverse relationship between algorithm performance and model complexity.