Improving the forecasting performance of temporal hierarchies
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 an...
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| Veröffentlicht in: | PloS one 2019-10, Vol.14 (10), p.e0223422-e0223422 |
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| creator | Spiliotis, Evangelos Petropoulos, Fotios Assimakopoulos, Vassilios |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0223422 |
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The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0223422</identifier><identifier>PMID: 31581211</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Bias ; Biology and Life Sciences ; Competition ; Computer and Information Sciences ; Computer engineering ; Data smoothing ; Displays (Marketing) ; Earth Sciences ; Forecasting ; Hierarchies ; Humans ; Inferential statistics ; Management science ; Methods ; Models, Statistical ; Multilevel analysis ; Performance evaluation ; Physical Sciences ; Reproducibility of Results ; Research and Analysis Methods ; Seasons ; Shrinkage ; Social Sciences ; Spatio-Temporal Analysis ; Time series</subject><ispartof>PloS one, 2019-10, Vol.14 (10), p.e0223422-e0223422</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Spiliotis et al. 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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. 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| subjects | Accuracy Algorithms Bias Biology and Life Sciences Competition Computer and Information Sciences Computer engineering Data smoothing Displays (Marketing) Earth Sciences Forecasting Hierarchies Humans Inferential statistics Management science Methods Models, Statistical Multilevel analysis Performance evaluation Physical Sciences Reproducibility of Results Research and Analysis Methods Seasons Shrinkage Social Sciences Spatio-Temporal Analysis Time series |
| title | Improving the forecasting performance of temporal hierarchies |
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