The M4 Competition: 100,000 time series and 61 forecasting methods

Ardia, 2018, Forecasting risk with Markov-switching GARCH models: a large-scale performance study, International Journal of Forecasting, 34, 733, 10.1016/j.ijforecast.2018.05.004

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Assimakopoulos, 2000, The theta model: a decomposition approach to forecasting, International Journal of Forecasting, 16, 521, 10.1016/S0169-2070(00)00066-2

Athanasopoulos, 2017, Forecasting with temporal hierarchies, European Journal of Operational Research, 262, 60, 10.1016/j.ejor.2017.02.046

Athanasopoulos, 2011, The tourism forecasting competition, International Journal of Forecasting, 27, 822, 10.1016/j.ijforecast.2010.04.009

Bates, 1969, The combination of forecasts, The Journal of the Operational Research Society, 20, 451, 10.1057/jors.1969.103

Bermúdez, 2010, Bayesian Forecasting with the Holt-Winters model, The Journal of the Operational Research Society, 61, 164, 10.1057/jors.2008.152

Box, 1970

Chan, 2018, Some theoretical results on forecast combinations, International Journal of Forecasting, 34, 64, 10.1016/j.ijforecast.2017.08.005

Chen, 2017, A new accuracy measure based on bounded relative error for time series forecasting, PLoS One, 12, 1, 10.1371/journal.pone.0174202

Claeskens, 2016, The forecast combination puzzle: a simple theoretical explanation, International Journal of Forecasting, 32, 754, 10.1016/j.ijforecast.2015.12.005

Clemen, 1989, Combining forecasts: a review and annotated bibliography with discussion, International Journal of Forecasting, 5, 559, 10.1016/0169-2070(89)90012-5

Coleman, 2007, On MAPE-R as a measure of cross-sectional estimation and forecast accuracy, Journal of Economic and Social Measurement, 32, 219, 10.3233/JEM-2007-0290

Crone, 2011, Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction, International Journal of Forecasting, 27, 635, 10.1016/j.ijforecast.2011.04.001

Davydenko, 2013, Measuring forecasting accuracy: the case of judgmental adjustments to SKU-level demand forecasts, International Journal of Forecasting, 29, 510, 10.1016/j.ijforecast.2012.09.002

Fiorucci, 2020, GROEC: combination method via generalized rolling origin evaluation, International Journal of Forecasting, 36, 105, 10.1016/j.ijforecast.2019.04.013

Fiorucci, 2016, Models for optimising the theta method and their relationship to state space models, International Journal of Forecasting, 32, 1151, 10.1016/j.ijforecast.2016.02.005

Franses, 2016, A note on the mean absolute scaled error, International Journal of Forecasting, 32, 20, 10.1016/j.ijforecast.2015.03.008

Gardner, 1985, Exponential smoothing: the state of the art, Journal of Forecasting, 4, 1, 10.1002/for.3980040103

Gardner, 2006, Exponential smoothing: The state of the art – Part II, International Journal of Forecasting, 22, 637, 10.1016/j.ijforecast.2006.03.005

Gneiting, 2007, Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, 102, 359, 10.1198/016214506000001437

Goodwin, 1999, On the asymmetry of the symmetric MAPE, International Journal of Forecasting, 15, 405, 10.1016/S0169-2070(99)00007-2

Hamzaçebi, 2009, Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting, Expert Systems with Applications, 36, 3839, 10.1016/j.eswa.2008.02.042

Hong, 2016, Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond, International Journal of Forecasting, 32, 896, 10.1016/j.ijforecast.2016.02.001

Hyndman, 2017

Hyndman, 2020, A brief history of forecasting competitions, International Journal of Forecasting, 36, 7, 10.1016/j.ijforecast.2019.03.015

Hyndman, 2008, Automatic time series forecasting: the forecast package for R, Journal of Statistical Software, 26, 1

Hyndman, 2006, Another look at measures of forecast accuracy, International Journal of Forecasting, 22, 679, 10.1016/j.ijforecast.2006.03.001

Hyndman, 2002, A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting, 18, 439, 10.1016/S0169-2070(01)00110-8

Ingel, 2020, Correlated daily time series and forecasting in the M4 competition, International Journal of Forecasting, 36, 121, 10.1016/j.ijforecast.2019.02.018

Jaganathan, 2020, Combination based forecasting method: M4 competition, International Journal of Forecasting, 36, 98, 10.1016/j.ijforecast.2019.03.030

Jakubovskis, 2017, Strategic facility location, capacity acquisition, and technology choice decisions under demand uncertainty: robust vs. non-robust optimization approaches, European Journal of Operational Research, 260, 1095, 10.1016/j.ejor.2017.01.017

Jose, 2008, Simple robust averages of forecasts: some empirical results, International Journal of Forecasting, 24, 163, 10.1016/j.ijforecast.2007.06.001

Kim, 2016, A new metric of absolute percentage error for intermittent demand forecasts, International Journal of Forecasting, 32, 669, 10.1016/j.ijforecast.2015.12.003

Kolassa, 2011, Combining exponential smoothing forecasts using Akaike weights, International Journal of Forecasting, 27, 238, 10.1016/j.ijforecast.2010.04.006

Kolassa, 2016, Evaluating predictive count data distributions in retail sales forecasting, International Journal of Forecasting, 32, 788, 10.1016/j.ijforecast.2015.12.004

Koning, 2005, The M3 competition: statistical tests of the results, International Journal of Forecasting, 21, 397, 10.1016/j.ijforecast.2004.10.003

Kourentzes, 2014, Improving forecasting by estimating time series structural components across multiple frequencies, International Journal of Forecasting, 30, 291, 10.1016/j.ijforecast.2013.09.006

M4 Team, 2018

Makridakis, 1993, Accuracy measures: theoretical and practical concerns, International Journal of Forecasting, 9, 527, 10.1016/0169-2070(93)90079-3

Makridakis, 1982, The accuracy of extrapolation (time series) methods: results of a forecasting competition, Journal of Forecasting, 1, 111, 10.1002/for.3980010202

Makridakis, 2018, Objectivity, reproducibility and replicability in forecasting research, International Journal of Forecasting, 34, 835, 10.1016/j.ijforecast.2018.05.001

Makridakis, 1993, The M2-competition: a real-time judgmentally based forecasting study, International Journal of Forecasting, 9, 5, 10.1016/0169-2070(93)90044-N

Makridakis, 1979, Accuracy of forecasting: an empirical investigation, Journal of the Royal Statistical Society, Series A, 142, 97, 10.2307/2345077

Makridakis, 2000, The M3-competition: results, conclusions and implications, International Journal of Forecasting, 16, 451, 10.1016/S0169-2070(00)00057-1

Makridakis, 1987, Confidence intervals: an empirical investigation of the series in the M-competition, International Journal of Forecasting, 3, 489, 10.1016/0169-2070(87)90045-8

Makridakis, 2009, Forecasting and uncertainty in the economic and business world, International Journal of Forecasting, 25, 794, 10.1016/j.ijforecast.2009.05.012

Makridakis, 2020, Forecasting in social settings: the state of the art, International Journal of Forecasting, 36, 15, 10.1016/j.ijforecast.2019.05.011

Makridakis, 2020, The m4 competition: Conclusions, International Journal of Forecasting, 36, 224, 10.1016/j.ijforecast.2019.05.006

Makridakis, 2018, Statistical and machine learning forecasting methods: concerns and ways forward, PLoS One, 13, 1, 10.1371/journal.pone.0194889

Makridakis, 2018, The M4 competition: results, findings, conclusions and ways forward, International Journal of Forecasting, 34, 802, 10.1016/j.ijforecast.2018.06.001

Makridakis, 2020, Predicting/hypothesizing the findings of the M4 competition?, International Journal of Forecasting, 36, 29, 10.1016/j.ijforecast.2019.02.012

Makridakis, 2009, Decision making and planning under low levels of predictability, International Journal of Forecasting, 25, 716, 10.1016/j.ijforecast.2009.05.013

Makridakis, 1998

Makridakis, 1983, Averages of forecasts: some empirical results, Management Science, 29, 987, 10.1287/mnsc.29.9.987

Montero-Manso, 2020, FFORMA: feature-based forecast model averaging, International Journal of Forecasting, 36, 86, 10.1016/j.ijforecast.2019.02.011

Montero-Manso, 2018

Moore, 1994, Using economic indicators to reduce risk in stock market investments, International Journal of Forecasting, 10, 405, 10.1016/0169-2070(94)90070-1

Nikolopoulos, 2018, Forecasting for big data: Does suboptimality matter?, Computers & Operations Research, 98, 322, 10.1016/j.cor.2017.05.007

Ord, 1997, Estimation and prediction for a class of dynamic nonlinear statistical models, Journal of the American Statistical Association, 92, 1621, 10.1080/01621459.1997.10473684

Pawlikowski, 2020, Weighted ensemble of statistical models, International Journal of Forecasting, 36, 93, 10.1016/j.ijforecast.2019.03.019

Petropoulos, 2018, Exploring the sources of uncertainty: Why does bagging for time series forecasting work?, European Journal of Operational Research, 268, 545, 10.1016/j.ejor.2018.01.045

Petropoulos, 2020, The M4 competition: Bigger. Stronger. Better, International Journal of Forecasting, 36, 3, 10.1016/j.ijforecast.2019.05.005

Petropoulos, 2020, A simple combination of univariate models, International Journal of Forecasting, 36, 110, 10.1016/j.ijforecast.2019.01.006

Petropoulos, 2019, The inventory performance of forecasting methods: Evidence from the M3 competition data, International Journal of Forecasting, 35, 251, 10.1016/j.ijforecast.2018.01.004

Prak, 2019, A general method for addressing forecasting uncertainty in inventory models, International Journal of Forecasting, 35, 224, 10.1016/j.ijforecast.2017.11.004

Salaken, 2017, Extreme learning machine based transfer learning algorithms: A survey, Neurocomputing, 267, 516, 10.1016/j.neucom.2017.06.037

Smith, 2009, A simple explanation of the forecast combination puzzle, Oxford Bulletin of Economics and Statistics, 71, 331, 10.1111/j.1468-0084.2008.00541.x

Smyl, 2020, A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting, International Journal of Forecasting, 36, 75, 10.1016/j.ijforecast.2019.03.017

Spiliotis, 2020, Are forecasting competitions data representative of the reality?, International Journal of Forecasting, 36, 37, 10.1016/j.ijforecast.2018.12.007

Spiliotis, 2019, Tales from tails: On the empirical distributions of forecasting errors and their implication to risk, International Journal of Forecasting, 35, 687, 10.1016/j.ijforecast.2018.10.004

Syntetos, 2010, Judging the judges through accuracy-implication metrics: The case of inventory forecasting, International Journal of Forecasting, 26, 134, 10.1016/j.ijforecast.2009.05.016

Taleb, 2009, Errors, robustness, and the fourth quadrant, International Journal of Forecasting, 25, 744, 10.1016/j.ijforecast.2009.05.027

Wucker, 2016

Zhang, 1998, Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting, 14, 35, 10.1016/S0169-2070(97)00044-7