Empirical information criteria for time series forecasting model selection
- 1 October 2005
- journal article
- research article
- Published by Informa UK Limited in Journal of Statistical Computation and Simulation
- Vol. 75 (10), 831-840
- https://doi.org/10.1080/00949650410001687208
Abstract
In this article, we propose a new empirical information criterion (EIC) for model selection which penalizes the likelihood of the data by a non-linear 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
This publication has 14 references indexed in Scilit:
- Unmasking the Theta methodInternational Journal of Forecasting, 2003
- A state space framework for automatic forecasting using exponential smoothing methodsInternational Journal of Forecasting, 2002
- The theta model: a decomposition approach to forecastingInternational Journal of Forecasting, 2000
- The M3-Competition: results, conclusions and implicationsInternational Journal of Forecasting, 2000
- Estimation and Prediction for a Class of Dynamic Nonlinear Statistical ModelsJournal of the American Statistical Association, 1997
- Global optimization of statistical functions with simulated annealingJournal of Econometrics, 1994
- A comparison of model selection criteriaEconometric Reviews, 1992
- The accuracy of extrapolation (time series) methods: Results of a forecasting competitionJournal of Forecasting, 1982
- Generalized Cross-Validation as a Method for Choosing a Good Ridge ParameterTechnometrics, 1979
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978