Improving the forecasting performance of temporal hierarchies


Autoři: Evangelos Spiliotis aff001;  Fotios Petropoulos aff002;  Vassilios Assimakopoulos aff001
Působiště autorů: Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece aff001;  School of Management, University of Bath, Bath, United Kingdom aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223422

Souhrn

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias.

Klíčová slova:

Covariance – Decision making – Forecasting – Machine learning algorithms – Mathematical functions – Seasons – Statistical methods – Extrapolation


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