Forecast combinations: An over 50-year review

[1]  Rob J. Hyndman,et al.  Meta‐learning how to forecast time series , 2023, Journal of Forecasting.

[2]  Yanfei Kang,et al.  Another look at forecast trimming for combinations: robustness, accuracy and diversity , 2022, 2208.00139.

[3]  David T. Frazier,et al.  The Impact of Sampling Variability on Estimated Combinations of Distributional Forecasts , 2022, 2206.02376.

[4]  E. Y. Cramer,et al.  Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States , 2022, International Journal of Forecasting.

[5]  Spyros Makridakis,et al.  M5 accuracy competition: Results, findings, and conclusions , 2022, International Journal of Forecasting.

[6]  R. L. Winkler,et al.  The M5 uncertainty competition: Results, findings and conclusions , 2021, International Journal of Forecasting.

[7]  Ben D. Fulcher,et al.  An Empirical Evaluation of Time-Series Feature Sets , 2021, 2021 International Conference on Data Mining Workshops (ICDMW).

[8]  Samantha V. Adams,et al.  Deep Learning to Improve Weather Predictions , 2021, Deep Learning for the Earth Sciences.

[9]  Yanfei Kang,et al.  Bayesian forecast combination using time-varying features , 2021, International Journal of Forecasting.

[10]  M. A. Ganaie,et al.  Ensemble deep learning: A review , 2021, Eng. Appl. Artif. Intell..

[11]  Alexander J. Smola,et al.  Flexible Model Aggregation for Quantile Regression , 2021, J. Mach. Learn. Res..

[12]  F. Ziel,et al.  CRPS Learning , 2021, Journal of Econometrics.

[13]  Fotios Petropoulos,et al.  Understanding forecast reconciliation , 2021, Eur. J. Oper. Res..

[14]  Robert Fildes,et al.  Retail sales forecasting with meta-learning , 2021, Eur. J. Oper. Res..

[15]  Evangelos Spiliotis,et al.  Investigating the accuracy of cross-learning time series forecasting methods , 2020, International Journal of Forecasting.

[16]  F. Diebold,et al.  On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates , 2020, Working paper (Federal Reserve Bank of Philadelphia).

[17]  Fotios Petropoulos,et al.  Forecasting: theory and practice , 2020, International Journal of Forecasting.

[18]  F. Petropoulos,et al.  Forecast with forecasts: Diversity matters , 2020, Eur. J. Oper. Res..

[19]  Nils Thuerey,et al.  Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench , 2020, Journal of Advances in Modeling Earth Systems.

[20]  Yi Wang,et al.  Load probability density forecasting by transforming and combining quantile forecasts , 2020, Applied Energy.

[21]  David T. Frazier,et al.  Optimal probabilistic forecasts: When do they work? , 2020, International Journal of Forecasting.

[22]  R. Tawn,et al.  Quantile Combination for the EEM20 Wind Power Forecasting Competition , 2020, 2020 17th International Conference on the European Energy Market (EEM).

[23]  Rob J Hyndman,et al.  Distributed ARIMA models for ultra-long time series , 2020, International Journal of Forecasting.

[24]  Thomas Setzer,et al.  Bias-Variance Trade-Off and Shrinkage of Weights in Forecast Combination , 2020, Manag. Sci..

[25]  Seungmin Rho,et al.  Combination of short-term load forecasting models based on a stacking ensemble approach , 2020 .

[26]  Johannes Bracher,et al.  Evaluating epidemic forecasts in an interval format , 2020, PLoS Comput. Biol..

[27]  Torsten Hoefler,et al.  Deep learning for post-processing ensemble weather forecasts , 2020, Philosophical Transactions of the Royal Society A.

[28]  S. Vannitsem,et al.  Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World , 2020, Bulletin of the American Meteorological Society.

[29]  Rob J. Hyndman,et al.  Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data , 2020 .

[30]  Sebastian Scher,et al.  Ensemble Methods for Neural Network‐Based Weather Forecasts , 2020, Journal of Advances in Modeling Earth Systems.

[31]  Ying Feng,et al.  For2For: Learning to forecast from forecasts , 2020, ArXiv.

[32]  Rob J. Hyndman,et al.  FFORMA: Feature-based forecast model averaging , 2020, International Journal of Forecasting.

[33]  Patrícia J. Bota,et al.  TSFEL: Time Series Feature Extraction Library , 2020, SoftwareX.

[34]  Amir F. Atiya,et al.  Why does forecast combination work so well? , 2020, International Journal of Forecasting.

[35]  Barbara Rossi,et al.  Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them , 2019, Journal of Economic Literature.

[36]  F. Bassetti,et al.  Density Forecasting , 2019, Macroeconomic Forecasting in the Era of Big Data.

[37]  Slawek Smyl,et al.  Machine learning methods for GEFCom2017 probabilistic load forecasting , 2019, International Journal of Forecasting.

[38]  Fotios Petropoulos,et al.  Déjà vu: A data-centric forecasting approach through time series cross-similarity , 2019 .

[39]  F. Petropoulos,et al.  The uncertainty estimation of feature-based forecast combinations , 2019, J. Oper. Res. Soc..

[40]  Leandro dos Santos Coelho,et al.  Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting , 2019, Eng. Appl. Artif. Intell..

[41]  Lilian M. de Menezes,et al.  Structural combination of seasonal exponential smoothing forecasts applied to load forecasting , 2019, Eur. J. Oper. Res..

[42]  Nicolas Chapados,et al.  N-BEATS: Neural basis expansion analysis for interpretable time series forecasting , 2019, ICLR.

[43]  Feng Li,et al.  Forecasting with time series imaging , 2019, Expert Syst. Appl..

[44]  Mary E. Thomson,et al.  Combining forecasts: Performance and coherence , 2019, International Journal of Forecasting.

[45]  Feng Li,et al.  GRATIS: GeneRAting TIme Series with diverse and controllable characteristics , 2019, Stat. Anal. Data Min..

[46]  Fotios Petropoulos,et al.  Another look at forecast selection and combination: Evidence from forecast pooling , 2019, International Journal of Production Economics.

[47]  Yael Grushka-Cockayne,et al.  Combining Prediction Intervals in the M4 Competition , 2019, International Journal of Forecasting.

[48]  Nick S. Jones,et al.  catch22: CAnonical Time-series CHaracteristics , 2019, Data Mining and Knowledge Discovery.

[49]  Stanley G. Benjamin,et al.  100 Years of Progress in Forecasting and NWP Applications , 2019, Meteorological Monographs.

[50]  Sebastian Scher,et al.  Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning , 2018, Geophysical Research Letters.

[51]  Laurent L. Pauwels,et al.  Higher Moment Constraints for Predictive Density Combination , 2018, SSRN Electronic Journal.

[52]  Agata Chorowska,et al.  Weighted ensemble of statistical models , 2018, 1811.07761.

[53]  Rob J. Hyndman,et al.  Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization , 2018, Journal of the American Statistical Association.

[54]  Francesco Ravazzolo,et al.  The Evolution of Forecast Density Combinations in Economics , 2018, Oxford Research Encyclopedia of Economics and Finance.

[55]  Sebastian Scher,et al.  Predicting weather forecast uncertainty with machine learning , 2018, Quarterly Journal of the Royal Meteorological Society.

[56]  Vivien Mallet,et al.  Ensemble forecast of photovoltaic power with online CRPS learning , 2018, International Journal of Forecasting.

[57]  Andreas W. Kempa-Liehr,et al.  Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) , 2018, Neurocomputing.

[58]  Francis X. Diebold,et al.  Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives , 2018, International Journal of Forecasting.

[59]  Fotios Petropoulos,et al.  Exploring the sources of uncertainty: Why does bagging for time series forecasting work? , 2018, Eur. J. Oper. Res..

[60]  Peter Bauer,et al.  Challenges and design choices for global weather and climate models based on machine learning , 2018, Geoscientific Model Development.

[61]  Stephan Rasp,et al.  Neural networks for post-processing ensemble weather forecasts , 2018, Monthly Weather Review.

[62]  Fotios Petropoulos,et al.  Judgmental selection of forecasting models , 2018 .

[63]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[64]  Daniel Kirschen,et al.  Combining Probabilistic Load Forecasts , 2018, IEEE Transactions on Smart Grid.

[65]  Philippe Naveau,et al.  Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting , 2017, Weather and Forecasting.

[66]  Mike West,et al.  Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting , 2017, Journal of the American Statistical Association.

[67]  Mark F.J. Steel,et al.  Model Averaging and Its Use in Economics , 2017, Journal of Economic Literature.

[68]  Nikolaos Kourentzes,et al.  Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels? , 2017 .

[69]  Aki Vehtari,et al.  Using Stacking to Average Bayesian Predictive Distributions (with Discussion) , 2017, Bayesian Analysis.

[70]  Kate Smith-Miles,et al.  Visualising forecasting algorithm performance using time series instance spaces , 2017 .

[71]  Yael Grushka-Cockayne,et al.  Bayesian Ensembles of Binary-Event Forecasts: When Is It Appropriate to Extremize or Anti-Extremize? , 2017, 1705.02391.

[72]  Yael Grushka-Cockayne,et al.  Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs , 2016, Oper. Res..

[73]  Fotios Petropoulos,et al.  Forecasting with multivariate temporal aggregation: the case of promotional modelling , 2016 .

[74]  Nick S. Jones,et al.  Automatic time-series phenotyping using massive feature extraction , 2016, bioRxiv.

[75]  Thomas Setzer,et al.  When to choose the simple average in forecast combination , 2016 .

[76]  Michael Vitale,et al.  The wisdom of crowds , 2016, The Lancet.

[77]  Sebastian Lerch,et al.  Combining predictive distributions for the statistical post-processing of ensemble forecasts , 2016, International Journal of Forecasting.

[78]  Tao Hong,et al.  GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation , 2016 .

[79]  Rob J Hyndman,et al.  Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation , 2016 .

[80]  Robert L. Winkler,et al.  Combining Interval Forecasts , 2016, Decis. Anal..

[81]  Mike West,et al.  Dynamic Bayesian predictive synthesis in time series forecasting , 2016, Journal of Econometrics.

[82]  Henry Mwambi,et al.  Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods , 2016 .

[83]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[84]  Ratnadip Adhikari A mutual association based nonlinear ensemble mechanism for time series forecasting , 2015, Applied Intelligence.

[85]  Yael Grushka-Cockayne,et al.  Ensembles of Overfit and Overconfident Forecasts , 2015, Manag. Sci..

[86]  Felix Chan,et al.  Some Theoretical Results on Forecast Combinations , 2015 .

[87]  Michel van der Wel,et al.  Combining Density Forecasts using Focused Scoring Rules , 2015 .

[88]  Craig A. Rolling,et al.  On the Forecast Combination Puzzle , 2015, Econometrics.

[89]  David V. Budescu,et al.  Aggregating multiple probability intervals to improve calibration , 2015, Judgment and Decision Making.

[90]  Roberto Casarin,et al.  Bayesian Nonparametric Calibration and Combination of Predictive Distributions , 2015, 1502.07246.

[91]  Eva Chen,et al.  Identifying Expertise to Extract the Wisdom of Crowds , 2015, Manag. Sci..

[92]  Enrique Moral-Benito,et al.  Model Averaging in Economics: An Overview , 2015 .

[93]  Fabio Busetti,et al.  Quantile Aggregation of Density Forecasts , 2014 .

[94]  Marco Del Negro,et al.  Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance , 2014 .

[95]  Andrey L. Vasnev,et al.  The Forecast Combination Puzzle: A Simple Theoretical Explanation , 2014 .

[96]  Fotios Petropoulos,et al.  'Horses for Courses' in demand forecasting , 2014, Eur. J. Oper. Res..

[97]  Richard P. Larrick,et al.  The wisdom of select crowds. , 2014, Journal of personality and social psychology.

[98]  Nikolaos Kourentzes,et al.  Neural network ensemble operators for time series forecasting , 2014, Expert Syst. Appl..

[99]  Lyle H. Ungar,et al.  Modeling Probability Forecasts via Information Diversity , 2014 .

[100]  Brandon M. Turner,et al.  Forecast aggregation via recalibration , 2014, Machine Learning.

[101]  Hongnian Yu,et al.  A combination selection algorithm on forecasting , 2014, Eur. J. Oper. Res..

[102]  F. Petropoulos,et al.  Improving forecasting by estimating time series structural components across multiple frequencies , 2014 .

[103]  George Kapetanios,et al.  Generalised Density Forecast Combinations , 2014 .

[104]  Michael P. Clements,et al.  Forecasting by factors, by variables, by both or neither? , 2013 .

[105]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[106]  Luca Delle Monache,et al.  Probabilistic Weather Prediction with an Analog Ensemble , 2013 .

[107]  Jakub Nowotarski,et al.  An Empirical Comparison of Alternate Schemes for Combining Electricity Spot Price Forecasts , 2013 .

[108]  Yael Grushka-Cockayne,et al.  The Wisdom of Competitive Crowds , 2013, Oper. Res..

[109]  Lutz Kilian,et al.  Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach , 2013 .

[110]  Araceli Sanchis,et al.  Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble , 2013, Neurocomputing.

[111]  Sándor Baran,et al.  Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components , 2013, Comput. Stat. Data Anal..

[112]  Laurent L. Pauwels,et al.  A Note on Estimation of Optimal Weights for Density Forecast Combinations , 2013 .

[113]  Yael Grushka-Cockayne,et al.  Trimmed Opinion Pools and the Crowd's Calibration Problem , 2012, Manag. Sci..

[114]  Vu,et al.  Time-Varying Combinations of Predictive Densities Using Nonlinear Filtering , 2012 .

[115]  Vladimir M. Krasnopolsky,et al.  A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US , 2012 .

[116]  C. De Mol,et al.  Optimal Combination of Survey Forecasts , 2012 .

[117]  Barbara Rossi,et al.  Advances in Forecasting Under Instability , 2012 .

[118]  R. K. Agrawal,et al.  A Novel Weighted Ensemble Technique for Time Series Forecasting , 2012, PAKDD.

[119]  Yael Grushka-Cockayne,et al.  Is it Better to Average Probabilities or Quantiles? , 2012, Manag. Sci..

[120]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[121]  Rob J. Hyndman,et al.  Optimal combination forecasts for hierarchical time series , 2011, Comput. Stat. Data Anal..

[122]  Dick van Dijk,et al.  Likelihood-based scoring rules for comparing density forecasts in tails , 2011 .

[123]  Amir F. Atiya,et al.  Combination of long term and short term forecasts, with application to tourism demand forecasting , 2011 .

[124]  T. Gneiting,et al.  Combining Predictive Distributions , 2011, 1106.1638.

[125]  S. Kolassa Combining exponential smoothing forecasts using Akaike weights , 2011 .

[126]  Jose Rodriguez,et al.  Forecast combination through dimension reduction techniques , 2011 .

[127]  H. Bondell,et al.  Noncrossing quantile regression curve estimation. , 2010, Biometrika.

[128]  Shaun P. Vahey,et al.  Combining forecast densities from VARs with uncertain instabilities , 2010 .

[129]  Bogdan Gabrys,et al.  Meta-learning for time series forecasting and forecast combination , 2010, Neurocomputing.

[130]  J. M. Sloughter,et al.  Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging , 2010 .

[131]  T. Gneiting,et al.  Combining probability forecasts , 2010 .

[132]  Robert L. Winkler,et al.  Evaluating Quantile Assessments , 2009, Oper. Res..

[133]  Michael P. Clements,et al.  Forecast Combination and Encompassing , 2009 .

[134]  K. Wallis,et al.  A Simple Explanation of the Forecast Combination Puzzle , 2009 .

[135]  Xiaozhe Wang,et al.  Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series , 2009, Neurocomputing.

[136]  Stephen M. Disney,et al.  Forecasting for inventory planning: a 50-year review , 2009, J. Oper. Res. Soc..

[137]  J. Geweke,et al.  Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns , 2008 .

[138]  J. Geweke,et al.  Optimal Prediction Pools , 2008 .

[139]  Xin Yao,et al.  Evolving artificial neural network ensembles , 2008, IEEE Computational Intelligence Magazine.

[140]  Mark A. Liniger,et al.  Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts? , 2007 .

[141]  Turgut Kisinbay,et al.  The Use of Encompassing Tests for Forecast Combinations , 2007 .

[142]  Andrew J. Patton,et al.  Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity , 2007 .

[143]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[144]  V. Chernozhukov,et al.  QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING , 2007, 0704.3649.

[145]  A. Raftery,et al.  Probabilistic forecasts, calibration and sharpness , 2007 .

[146]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[147]  Allan Timmermann,et al.  Persistence in forecasting performance and conditional combination strategies , 2006 .

[148]  Todd E. Clark,et al.  Averaging Forecasts from Vars with Uncertain Instabilities , 2006 .

[149]  Paulo S. A. Freitas,et al.  Model combination in neural-based forecasting , 2006, Eur. J. Oper. Res..

[150]  Jeremy E. Oakley,et al.  Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .

[151]  Kenneth F. Wallis,et al.  Combining Density and Interval Forecasts: A Modest Proposal , 2005 .

[152]  Adrian E. Raftery,et al.  Weather Forecasting with Ensemble Methods , 2005, Science.

[153]  Anton H. Westveld,et al.  Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation , 2005 .

[154]  R. Buizza,et al.  A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems , 2005 .

[155]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[156]  Jesús Fernández-Villaverde,et al.  Comparing dynamic equilibrium models to data: a Bayesian approach , 2004 .

[157]  J. Stock,et al.  Combination forecasts of output growth in a seven-country data set , 2004 .

[158]  Rich Caruana,et al.  Ensemble selection from libraries of models , 2004, ICML.

[159]  Ajith Abraham,et al.  An ensemble of neural networks for weather forecasting , 2004, Neural Computing & Applications.

[160]  Michael P. Clements,et al.  Pooling of Forecasts , 2004 .

[161]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[162]  Stephen C. Hora,et al.  Probability Judgments for Continuous Quantities: Linear Combinations and Calibration , 2004, Manag. Sci..

[163]  M. Fitzgerald,et al.  Horses for courses. , 2004, International journal of nursing practice.

[164]  Anthony Garratt,et al.  Forecast Uncertainties in Macroeconomic Modeling , 2003 .

[165]  Jonathan H. Wright,et al.  Bayesian Model Averaging and Exchange Rate Forecasts , 2003 .

[166]  F. Atger,et al.  Spatial and Interannual Variability of the Reliability of Ensemble-Based Probabilistic Forecasts: Consequences for Calibration , 2003 .

[167]  J. Stock,et al.  How Did Leading Indicator Forecasts Perform during the 2001 Recession? , 2003 .

[168]  Gary Koop,et al.  Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging , 2003 .

[169]  Clifford F. Mass,et al.  IFPS and the Future of the National Weather Service , 2003 .

[170]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[171]  Allan Timmermann,et al.  Optimal Forecast Combinations Under General Loss Functions and Forecast Error Distributions , 2002 .

[172]  K. Nikolopoulos,et al.  The theta model: a decomposition approach to forecasting , 2000 .

[173]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[174]  H. V. Dijk,et al.  Combined forecasts from linear and nonlinear time series models , 1999 .

[175]  J. Stock,et al.  A dynamic factor model framework for forecast combination , 1999 .

[176]  Nigel Harvey,et al.  Combining forecasts: What information do judges need to outperform the simple average? , 1999 .

[177]  Robert L. Winkler,et al.  Combining Probability Distributions From Experts in Risk Analysis , 1999 .

[178]  Anthony S. Tay,et al.  Evaluating Density Forecasts with Applications to Financial Risk Management , 1998 .

[179]  J. Stock,et al.  A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series , 1998 .

[180]  P. Newbold,et al.  Tests for Forecast Encompassing , 1998 .

[181]  Anthony S. Tay,et al.  Evaluating Density Forecasts , 1997 .

[182]  Paul G. Harrald,et al.  Evolving artificial neural networks to combine financial forecasts , 1997, IEEE Trans. Evol. Comput..

[183]  D. Madigan,et al.  Bayesian Model Averaging for Linear Regression Models , 1997 .

[184]  Spyros Makridakis Forecasting: its role and value for planning and strategy , 1996 .

[185]  Robert T. Clemen,et al.  Copula Models for Aggregating Expert Opinions , 1996, Oper. Res..

[186]  R. L. Winkler,et al.  Coherent combination of experts' opinions , 1995 .

[187]  Roy Batchelor,et al.  Forecaster diversity and the benefits of combining forecasts , 1995 .

[188]  Timo Teräsvirta,et al.  The combination of forecasts using changing weights , 1994 .

[189]  Robert L. Winkler,et al.  Aggregating Point Estimates: A Flexible Modeling Approach , 1993 .

[190]  Arnold Zellner,et al.  To combine or not to combine? Issues of combining forecasts , 1992 .

[191]  Stephen K. McNees The uses and abuses of ‘consensus’ forecasts , 1992 .

[192]  Fred Collopy,et al.  Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations , 1992 .

[193]  M. West,et al.  Modelling Probabilistic Agent Opinion , 1992 .

[194]  S. I. Gunter Nonnegativity restricted least squares combinations , 1992 .

[195]  Sevket I. Gunter,et al.  An empirical analysis of the accuracy of SA, OLS, ERLS and NRLS combination forecasts , 1992 .

[196]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[197]  Francis X. Diebold,et al.  The use of prior information in forecast combination , 1990 .

[198]  D. Hendry,et al.  Econometric Evaluation of Linear Macro-Economic Models , 1986 .

[199]  Heejoon Kang Unstable Weights in the Combination of Forecasts , 1986 .

[200]  Christian Genest,et al.  Modeling Expert Judgments for Bayesian Updating , 1985 .

[201]  Robert L. Winkler,et al.  Limits for the Precision and Value of Information from Dependent Sources , 1985, Oper. Res..

[202]  C. Granger,et al.  Improved methods of combining forecasts , 1984 .

[203]  A. P. Dawid,et al.  Present position and potential developments: some personal views , 1984 .

[204]  R. L. Winkler,et al.  Averages of Forecasts: Some Empirical Results , 1983 .

[205]  R. L. Winkler,et al.  The Combination of Forecasts , 1983 .

[206]  Robert L. Winkler,et al.  The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[207]  R. L. Winkler Combining Probability Distributions from Dependent Information Sources , 1981 .

[208]  Brian H. Ross,et al.  On appropriate procedures for combining probability distributions within the same family , 1980 .

[209]  Edward E. Leamer,et al.  Specification Searches: Ad Hoc Inference with Nonexperimental Data , 1980 .

[210]  R. Ratcliff Group reaction time distributions and an analysis of distribution statistics. , 1979, Psychological bulletin.

[211]  S. Taylor Forecasting Economic Time Series , 1979 .

[212]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[213]  Peter A. Morris,et al.  Combining Expert Judgments: A Bayesian Approach , 1977 .

[214]  D. W. Bunn,et al.  A Bayesian Approach to the Linear Combination of Forecasts , 1975 .

[215]  H. Akaike A new look at the statistical model identification , 1974 .

[216]  Peter A. Morris,et al.  Decision Analysis Expert Use , 1974 .

[217]  C. Granger,et al.  Experience with Forecasting Univariate Time Series and the Combination of Forecasts , 1974 .

[218]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[219]  Robert L. Winkler,et al.  The Consensus of Subjective Probability Distributions , 1968 .

[220]  Erik Ruist,et al.  Forecasting in Theory and Practice , 1965 .

[221]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[222]  M. Stone The Opinion Pool , 1961 .

[223]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[224]  F. Galton One Vote, One Value , 1907, Nature.

[225]  A. Raftery,et al.  Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold , 2007 .

[226]  Evangelos Spiliotis,et al.  The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting , 2021 .

[227]  J. Armstrong,et al.  PRINCIPLES OF FORECASTING 1 Principles of Forecasting : A Handbook for Researchers and Practitioners , 2006 .

[228]  Alex Smola,et al.  Deep Quantile Aggregation , 2021, ArXiv.

[229]  Evangelos Spiliotis,et al.  The M4 Competition: 100,000 time series and 61 forecasting methods , 2020 .

[230]  David Shaub,et al.  Fast and accurate yearly time series forecasting with forecast combinations , 2020 .

[231]  Leandro dos Santos Coelho,et al.  Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series , 2020, Appl. Soft Comput..

[232]  Robert L. Winkler,et al.  Why do some combinations perform better than others? , 2020 .

[233]  Fotios Petropoulos,et al.  A simple combination of univariate models , 2020 .

[234]  Rob J. Hyndman,et al.  A brief history of forecasting competitions , 2020 .

[235]  Juan R. Trapero,et al.  Quantile forecast optimal combination to enhance safety stock estimation , 2019, International Journal of Forecasting.

[236]  Kevin J. Wilson,et al.  An investigation of dependence in expert judgement studies with multiple experts , 2017 .

[237]  Paul Baudin,et al.  Online learning with the Continuous Ranked Probability Score for ensemble forecasting , 2017 .

[238]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[239]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[240]  K. Lahiri,et al.  Testing the Value of Probability Forecasts for Calibrated Combining. , 2015, International journal of forecasting.

[241]  Robert Fildes,et al.  Simple versus complex forecasting : The evidence , 2015 .

[242]  Shui Ki Wan,et al.  Is there an optimal forecast combination , 2014 .

[243]  A. Timmermann,et al.  Combining expert forecasts: Can anything beat the simple average? , 2013 .

[244]  Graham Elliott,et al.  Averaging and the Optimal Combination of Forecasts , 2011 .

[245]  Jonathan H. Wright,et al.  Forecasting Inflation , 2011 .

[246]  G. Koop,et al.  Forecasting In ation Using Dynamic Model Averaging , 2009 .

[247]  Robert L. Winkler,et al.  Simple robust averages of forecasts: Some empirical results , 2008 .

[248]  David E. Rapach,et al.  Forecasting US employment growth using forecast combining methods , 2008 .

[249]  S. Hall,et al.  Combining density forecasts , 2007 .

[250]  Ronald Jones Déja vu. , 2006, Veterinary anaesthesia and analgesia.

[251]  A. Timmermann Chapter 4 Forecast Combinations , 2006 .

[252]  Richard P. Larrick,et al.  Intuitions About Combining Opinions: Misappreciation of the Averaging Principle , 2006, Manag. Sci..

[253]  T. Evgeniou,et al.  To combine or not to combine: selecting among forecasts and their combinations , 2005 .

[254]  Michael P. Clements,et al.  A companion to economic forecasting , 2004 .

[255]  Derek W. Bunn,et al.  Review of guidelines for the use of combined forecasts , 2000, Eur. J. Oper. Res..

[256]  A. Zellner Keep It Sophisticatedly Simple , 1999 .

[257]  Li Da Xu,et al.  Improving the accuracy of nonlinear combined forecasting using neural networks , 1999 .

[258]  Mark J. Kamstra,et al.  Forecast combining with neural networks , 1996 .

[259]  J. Graham Is a Group of Economists Better than One? Than None? , 1996 .

[260]  N. Edward Coulson,et al.  Forecast combination in a dynamic setting , 1993 .

[261]  M. West,et al.  Modelling Agent Forecast Distributions , 1992 .

[262]  Francis X. Diebold,et al.  Forecast combination and encompassing: Reconciling two divergent literatures , 1989 .

[263]  Francis X. Diebold,et al.  Serial Correlation and the Combination of Forecasts , 1988 .

[264]  F. Diebold,et al.  Structural change and the combination of forecasts , 1986 .

[265]  R. L. Winkler,et al.  Combining Economic Forecasts , 1986 .

[266]  D. Bunn,et al.  Statistical efficiency in the linear combination of forecasts , 1985 .

[267]  C. E. Agnew,et al.  Bayesian consensus forecasts of macroeconomic variables , 1985 .

[268]  Rand R. Wilcox,et al.  The statistical implications of pre-test and Stein-rule estimators in econometrics , 1978 .

[269]  N. Sugiura Further analysts of the data by akaike' s information criterion and the finite corrections , 1978 .

[270]  Lars-Erik Öller,et al.  A Method for Pooling Forecasts , 1978 .

[271]  S. B. Vincent The function of the vibrissae in the behavior of the white rat , 1912 .

[272]  F. Galton Vox Populi , 1907, Nature.

[273]  R. Clemen Combining Forecasts: a Review and Annotated , 2022 .

[274]  Dirk Van,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[275]  Massa,et al.  Deterministic Nonperiodic Flow 1 , 2022 .