Working Papers 2003 – Abstracts
Empirical Information Criteria for Time Series Forecasting Model Selection
Md B. Billah, R. J. Hyndman and A. B. Koehler
In this paper, we propose a new Empirical Information Criterion (EIC)
for model selection which penalizes the likelihood of the data by a function
of the number of parameters in the model. It is designed to be used where
there are a large number of time series to be forecast. However, a bootstrap
version of the EIC can be used where there is a single time series to
be forecast. The EIC provides a data-driven model selection tool that
can be tuned to the particular forecasting task. We compare the EIC with
other model selection criteria including Akaike's Information Criterion
(AIC) and Schwarz's Bayesian Information Criterion (BIC). The comparisons
show that for the M3 forecasting competition data, the EIC outperforms
both the AIC and BIC, particularly for longer forecast horizons. We also
compare the criteria on simulated data and find that the EIC does better
than existing criteria in that case also.
Keywords: Exponential smoothing; forecasting; information criteria;
M3 competition; model selection.
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