Improving forecasting by subsampling seasonal time series

@article{Li2022ImprovingFB,
  title={Improving forecasting by subsampling seasonal time series},
  author={Xixi Li and Fotios Petropoulos and Yanfei Kang},
  journal={International Journal of Production Research},
  year={2022}
}
Time series forecasting plays an increasingly important role in modern business decisions. In today’s data-rich environment, people often aim to choose the optimal forecasting model for their data. However, identifying the optimal model requires professional knowledge and experience, making accurate forecasting a challenging task. To mitigate the importance of model selection, we propose a simple and reliable algorithm to improve the forecasting performance. Specifically, we construct multiple… 
Exponential smoothing forecasts: taming the bullwhip effect when demand is seasonal
In this paper, we study the influence of seasonal demands and forecasts on the performance of an Automatic Pipeline, Variable Inventory, Order-Based, Production Control System (APVIOBPCS) using

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