The M4 Competition: 100,000 time series and 61 forecasting methods (Englisch)
- Neue Suche nach: Makridakis, Spyros
- Neue Suche nach: Spiliotis, Evangelos
- Neue Suche nach: Assimakopoulos, Vassilios
- Neue Suche nach: Makridakis, Spyros
- Neue Suche nach: Spiliotis, Evangelos
- Neue Suche nach: Assimakopoulos, Vassilios
In:
International Journal of Forecasting
;
36
, 1
;
54-74
;
2019
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ISSN:
- Aufsatz (Zeitschrift) / Elektronische Ressource
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Titel:The M4 Competition: 100,000 time series and 61 forecasting methods
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Beteiligte:Makridakis, Spyros ( Autor:in ) / Spiliotis, Evangelos ( Autor:in ) / Assimakopoulos, Vassilios ( Autor:in )
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Erschienen in:International Journal of Forecasting ; 36, 1 ; 54-74
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Verlag:
- Neue Suche nach: The Author(s)
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Erscheinungsdatum:23.04.2019
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Format / Umfang:21 pages
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:
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Datenquelle:
Inhaltsverzeichnis – Band 36, Ausgabe 1
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- 1
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Foreword to the M4 CompetitionTaleb, Nassim Nicholas et al. | 2019
- 3
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The M4 competition: Bigger. Stronger. BetterPetropoulos, Fotios / Makridakis, Spyros et al. | 2019
- 7
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A brief history of forecasting competitionsHyndman, Rob J. et al. | 2019
- 15
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Forecasting in social settings: The state of the artMakridakis, Spyros / Hyndman, Rob J. / Petropoulos, Fotios et al. | 2019
- 29
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Predicting/hypothesizing the findings of the M4 CompetitionMakridakis, Spyros / Spiliotis, Evangelos / Assimakopoulos, Vassilios et al. | 2019
- 37
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Are forecasting competitions data representative of the reality?Spiliotis, Evangelos / Kouloumos, Andreas / Assimakopoulos, Vassilios / Makridakis, Spyros et al. | 2019
- 54
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The M4 Competition: 100,000 time series and 61 forecasting methodsMakridakis, Spyros / Spiliotis, Evangelos / Assimakopoulos, Vassilios et al. | 2019
- 75
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A hybrid method of exponential smoothing and recurrent neural networks for time series forecastingSmyl, Slawek et al. | 2019
- 86
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FFORMA: Feature-based forecast model averagingMontero-Manso, Pablo / Athanasopoulos, George / Hyndman, Rob J. / Talagala, Thiyanga S. et al. | 2019
- 93
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Weighted ensemble of statistical modelsPawlikowski, Maciej / Chorowska, Agata et al. | 2019
- 98
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A combination-based forecasting method for the M4-competitionJaganathan, Srihari / Prakash, P.K.S. et al. | 2019
- 105
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GROEC: Combination method via Generalized Rolling Origin EvaluationFiorucci, Jose Augusto / Louzada, Francisco et al. | 2019
- 110
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A simple combination of univariate modelsPetropoulos, Fotios / Svetunkov, Ivan et al. | 2019
- 116
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Fast and accurate yearly time series forecasting with forecast combinationsShaub, David et al. | 2019
- 121
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Correlated daily time series and forecasting in the M4 competitionIngel, Anti / Shahroudi, Novin / Kängsepp, Markus / Tättar, Andre / Komisarenko, Viacheslav / Kull, Meelis et al. | 2019
- 129
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Card forecasts for M4Doornik, Jurgen A. / Castle, Jennifer L. / Hendry, David F. et al. | 2019
- 135
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Forecasting the M4 competition weekly data: Forecast Pro’s winning approachDarin, Sarah Goodrich / Stellwagen, Eric et al. | 2019
- 142
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Why do some combinations perform better than others?Lichtendahl, Kenneth C. Jr. / Winkler, Robert L. et al. | 2019
- 150
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Machine learning in M4: What makes a good unstructured model?Barker, Jocelyn et al. | 2019
- 156
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The M4 forecasting competition – A practitioner’s viewFry, Chris / Brundage, Michael et al. | 2019
- 161
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The value added by machine learning approaches in forecastingGilliland, Michael et al. | 2019
- 167
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Criteria for classifying forecasting methodsJanuschowski, Tim / Gasthaus, Jan / Wang, Yuyang / Salinas, David / Flunkert, Valentin / Bohlke-Schneider, Michael / Callot, Laurent et al. | 2019
- 178
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Combining prediction intervals in the M4 competitionGrushka-Cockayne, Yael / Jose, Victor Richmond R. et al. | 2019
- 186
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Learning from forecasting competitionsFildes, Robert et al. | 2019
- 189
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Performance measurement in the M4 Competition: Possible future researchGoodwin, Paul et al. | 2019
- 191
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Forecasting with high frequency data: M4 competition and beyondHong, Tao et al. | 2019
- 195
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Data adjustments, overfitting and representativenessOrd, Keith et al. | 2019
- 197
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Why does forecast combination work so well?Atiya, Amir F. et al. | 2019
- 201
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Comments on M4 competitionBontempi, Gianluca et al. | 2019
- 203
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On the M4.0 forecasting competition: Can you tell a 4.0 earthquake from a 3.0?Nikolopoulos, Konstantinos / Thomakos, Dimitrios D. / Katsagounos, Ilias / Alghassab, Waleed et al. | 2019
- 206
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M4 competition: What’s next?Önkal, Dilek et al. | 2019
- 208
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Why the “best” point forecast depends on the error or accuracy measureKolassa, Stephan et al. | 2019
- 212
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Correlation analysis of forecasting methods: The case of the M4 competitionAgathangelou, Pantelis / Trihinas, Demetris / Katakis, Ioannis et al. | 2019
- 217
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Responses to discussions and commentariesMakridakis, Spyros / Spiliotis, Evangelos / Assimakopoulos, Vassilios et al. | 2019
- 224
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The M4 competition: ConclusionsMakridakis, Spyros / Petropoulos, Fotios et al. | 2019
- iv
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Editorial Board| 2019