SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Kaggle forecasting competitions: An overlooked learning opportunity
10 references·16 citing·37 second hop
Kaggle forecasting competitions: An overlooked learning opportunity (2020)Kaggle forecasting competitions: An overl…The M4 Competition: 100,000 time series and 61 forecasting methods (2019)The M4 Competition: 100,000 t…Retail forecasting: Research and practice (2019)Retail forecasting: Research …FFORMA: Feature-based forecast model averaging (2019)FFORMA: Feature-based forecas…DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)DeepAR: Probabilistic forecas…A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)A hybrid method of exponentia…Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011)Advances in forecasting with …The tourism forecasting competition (2010)The tourism forecasting compe…Another look at measures of forecast accuracy (2006)Another look at measures of f…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …The Combination of Forecasts (1969)The Combination of ForecastsTransformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables (2025)Transformer-Based Models for …Supervised machine learning for theory building and testing: Opportunities in operations management (2023)Supervised machine learning f…Simplifying tree-based methods for retail sales forecasting with explanatory variables (2023)Simplifying tree-based method…Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities (2022)Understanding machine learnin…Time Series and Regression Methods for Univariate Environmental Forecasting: An Empirical Evaluation (2022)Time Series and Regression Me…The future of forecasting competitions: Design attributes and principles (2022)The future of forecasting com…Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward (2022)Statistical, machine learning…Optimizing inventory control through a data-driven and model-independent framework (2022)Optimizing inventory control …Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training (2021)Web Traffic Time Series Forec…The M5 uncertainty competition: Results, findings and conclusions (2021)The M5 uncertainty competitio…The M5 competition: Background, organization, and implementation (2021)The M5 competition: Backgroun…Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions (2021)Machine Learning Outperforms …Global models for time series forecasting: A Simulation study (2021)Global models for time series…Forecasting with trees (2021)Forecasting with treesCross‐item learning for volatile demand forecasting: An intervention with predictive analytics (2021)Cross‐item learning for volat…Comparison of statistical and machine learning methods for daily SKU demand forecasting (2020)Comparison of statistical and…Combining forecasts: A review and annotated bibliography (1989)Macroeconomics and Reality (1980)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)A meta-analysis of international tourism demand forecasting and implications for practice (2014)New developments in tourism and hotel demand modeling and forecasting (2017)Fuzzy sets (1965)Fuzzy sets (1996)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Toward Automatic Time-Series Forecasting Using Neural Networks (2012)‘Horses for Courses’ in demand forecasting (2014)Simple versus complex forecasting: The evidence (2015)Forecasting with artificial neural networks: (1998)Designing and managing the supply chain : concepts, strategies, and case studies (2000)Automatic Time Series Forecasting: The forecast Package for R (2008)N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)Forecasting: theory and practice (2022)Forecasting Using Principal Components From a Large Number of Predictors (2002)The Effect of Word of Mouth on Sales: Online Book Reviews (2006)Application of Artificial Intelligence in Automation of Supply Chain Management (2019)Daily retail demand forecasting using machine learning with emphasis on calendric special days (2020)Retail sales forecasting with meta-learning (2020)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Criteria for classifying forecasting methods (2019)Principles and algorithms for forecasting groups of time series: Locality and globality (2021)Forecast combinations: An over 50-year review (2022)A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017)The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms (2017)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)M5 accuracy competition: Results, findings, and conclusions (2022)Time Series Analysis Forecasting and Control. (1971)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Time series forecasting using a hybrid ARIMA and neural network model (2003)25 years of time series forecasting (2006)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
this work references cited by second hop

References

  • 2006Another look at measures of forecast accuracy5,736 cited
  • 1969The Combination of Forecasts3,194 cited
  • 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks2,726 cited
  • 2000The M3-Competition: results, conclusions and implications1,700 cited
  • 2019The M4 Competition: 100,000 time series and 61 forecasting methods990 cited
  • 2019A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting766 cited
  • 2019Retail forecasting: Research and practice357 cited
  • 2019FFORMA: Feature-based forecast model averaging313 cited
  • 2010The tourism forecasting competition286 cited
  • 2011Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction272 cited

Cited by

  • 2021The M5 competition: Background, organization, and implementation143 cited
  • 2022Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward134 cited
  • 2021Forecasting with trees117 cited
  • 2020Comparison of statistical and machine learning methods for daily SKU demand forecasting111 cited
  • 2021The M5 uncertainty competition: Results, findings and conclusions

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

103 cited
  • 2021Global models for time series forecasting: A Simulation study83 cited
  • 2021Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training69 cited
  • 2022Time Series and Regression Methods for Univariate Environmental Forecasting: An Empirical Evaluation65 cited
  • 2023Supervised machine learning for theory building and testing: Opportunities in operations management57 cited
  • 2021Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions48 cited
  • 2021Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics44 cited
  • 2022Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities40 cited
  • 2023Simplifying tree-based methods for retail sales forecasting with explanatory variables40 cited
  • 2025Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables37 cited
  • 2022Optimizing inventory control through a data-driven and model-independent framework36 cited
  • 2022The future of forecasting competitions: Design attributes and principles33 cited