Adaptive Order Determination for Constructing Time Series Forecasting Models |
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Authors: | Yongli Zhang |
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Institution: | Department of Operations and Business Analytics, Lundquist College of Business, University of Oregon, Eugene, Oregon, USA |
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Abstract: | In time series modeling consistent criteria like Bayesian Information Criterion (BIC) outperform in terms of predictability loss-efficient criteria like Akaike Information Criterion (AIC) when data are generated by a finite-order autoregressive process, and the reverse is true when data are generated by an infinite-order autoregressive process. Since in practice we don’t know the data-generating process, it is useful to have an adaptive criterion that behaves as either a consistent or just as a loss-efficient criterion, whichever performs better. Here we derive such a criterion. Moreover, our criterion is adaptive to effective sample sizes and not sensitive to maximum a priori determined order limits. |
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Keywords: | Autoregressive Moving Average (ARMA) ARMA processes Autoregressive representations Effective sample size Maximum a priori determined order limits Robustness |
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