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Machine learning in M4: What makes a good unstructured model?
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Machine learning in M4: What makes a good unstructured model? (2019)Machine learning in M4: What makes a good…DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)DeepAR: Probabilistic forecas…Are forecasting competitions data representative of the reality? (2019)Are forecasting competitions …The M4 Competition: Results, findings, conclusion and way forward (2018)The M4 Competition: Results, …Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Statistical and Machine Learn…Visualising forecasting algorithm performance using time series instance spaces (2017)Visualising forecasting algor…Automatic time series forecasting: the forecast package for R (2017)Automatic time series forecas…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future (2023)Time Series Forecasting with …Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins (2023)Comparative Analysis of Machi…Forecasting: theory and practice (2022)Forecasting: theory and pract…Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics (2021)Cross‐item learning for volat…A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting (2020)A New Bootstrapped Hybrid Art…Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Another look at measures of forecast accuracy (2006)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Forecasting principles and practice (2018)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)Time Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control. (1978)N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020)Book Review: Time Series Analysis: Forecasting and Control (1977)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The M3-Competition: results, conclusions and implications (2000)Forecasting methods and applications (1993)Fuzzy sets (1965)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)User Acceptance of Computer Technology: A Comparison of Two Theoretical Models (1989)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)Forecast combinations: An over 50-year review (2022)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Time series forecasting using a hybrid ARIMA and neural network model (2003)Case study research: design and methods (2011)Empirical research methods department: Mission, learnings, and future plans (2022)Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions (2024)Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages (2024)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Forecasting at Scale (2017)
this work references cited by second hop

References

  • 2008Automatic Time Series Forecasting: The forecast Package for R3,665 cited
  • 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks2,851 cited
  • 2018Statistical and Machine Learning forecasting methods: Concerns and ways forward1,659 cited
  • 2017Automatic time series forecasting: the forecast package for R1,019 cited
  • 2018The M4 Competition: Results, findings, conclusion and way forward683 cited
  • 2017Visualising forecasting algorithm performance using time series instance spaces157 cited
  • 2019Are forecasting competitions data representative of the reality?67 cited

Cited by

  • 2022Forecasting: theory and practice1,041 cited
  • 2021Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics44 cited
  • 2023Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins32 cited
  • 2023Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future27 cited
  • 2020A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting25 cited

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