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Explainable boosted linear regression for time series forecasting
15 references·4 citing·42 second hop
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References

  • 1978Time Series Analysis: Forecasting and Control.5,299 cited
  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
  • 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks2,726 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
  • 2020Probabilistic forecasting with temporal convolutional neural network399 cited
  • 2019Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model337 cited
  • 2005Improved supply chain management based on hybrid demand forecasts312 cited
  • 2001Forecasting aggregate retail sales:258 cited
  • 2003A comparative study of linear and nonlinear models for aggregate retail sales forecasting246 cited
  • 2005A comparative study of autoregressive neural network hybrids219 cited
  • 2015A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting191 cited
  • 2020Retail sales forecasting with meta-learning180 cited
  • 2010Financial time series forecasting with machine learning techniques: A survey157 cited

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  • 2009Forecasting nonlinear time series with a hybrid methodology155 cited
  • Cited by

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    • 2024AdaBoost.RDT: AdaBoost Integrated With Residual-Based Decision Tree for Demand Prediction of Bike Sharing Systems Under Extreme Demands4 cited