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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Simple versus complex selection rules for forecasting many time series
AU - Fildes, Robert
AU - Petropoulos, Fotios
PY - 2015/8
Y1 - 2015/8
N2 - Selecting the appropriate forecasting method for a large number of time series is a major problem for many organizational forecaster. Researchers propose various selection rules in order to enhance forecasting accuracy. The simpler approach for model selection involves the identification of a single method, which is applied to all data series in an aggregate manner, without taking into account the specific characteristics of a single series. On the other hand, individual selection includes the identification of the best method for each series, though it is more computationally intensive. A simple combination of methods also provides an operational benchmark. The current study explores the circumstances under which individual model selection is beneficial and when this approach should be preferred to aggregate selection or combination. The superiority of each approach is analyzed in terms of data characteristics, existence or not of a dominant method and stability of the competing methods' comparative performance. In addition, the size and composition of the pools of methods under consideration are examined. In order to assess the efficacy of individual model selection in the cases considered, simple selection rules are proposed, based on within-sample best fit or best forecasting performance for different forecast horizons. The analysis shows that individual selection works best when specific sub-populations of data are considered (e.g., trended or seasonal series), but also when the alternative methods' comparative performance is stable over time. A case study demonstrates the efficiency of the recommended selection strategy.
AB - Selecting the appropriate forecasting method for a large number of time series is a major problem for many organizational forecaster. Researchers propose various selection rules in order to enhance forecasting accuracy. The simpler approach for model selection involves the identification of a single method, which is applied to all data series in an aggregate manner, without taking into account the specific characteristics of a single series. On the other hand, individual selection includes the identification of the best method for each series, though it is more computationally intensive. A simple combination of methods also provides an operational benchmark. The current study explores the circumstances under which individual model selection is beneficial and when this approach should be preferred to aggregate selection or combination. The superiority of each approach is analyzed in terms of data characteristics, existence or not of a dominant method and stability of the competing methods' comparative performance. In addition, the size and composition of the pools of methods under consideration are examined. In order to assess the efficacy of individual model selection in the cases considered, simple selection rules are proposed, based on within-sample best fit or best forecasting performance for different forecast horizons. The analysis shows that individual selection works best when specific sub-populations of data are considered (e.g., trended or seasonal series), but also when the alternative methods' comparative performance is stable over time. A case study demonstrates the efficiency of the recommended selection strategy.
KW - automatic model selection
KW - comparative methods
KW - extrapolative methods
KW - combination
KW - stability
U2 - 10.1016/j.jbusres.2015.03.028
DO - 10.1016/j.jbusres.2015.03.028
M3 - Journal article
VL - 68
SP - 1692
EP - 1701
JO - Journal of Business Research
JF - Journal of Business Research
SN - 0148-2963
IS - 5
ER -