Abstract
Abstract. In this paper we propose the order determination quantity (ODQ) as a new way to solve order estimation problems in time series analysis. We estimate orders according to ODQ > 0 or ODQ < 0 instead of by minimizing. Theoretical analysis and simulation have shown that the ODQ has higher identifiability for unknown true orders, provides clear separation points and requires less computational effort than the existing order estimation criteria such as Akaike's information criterion (AIC), Bayes information criterion (BIC), φ and predictive least squares (PLS).
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