Corpus ID: 221005773

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

@article{Bandara2020ImprovingTA,
  title={Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation},
  author={Kasun Bandara and Hansika Hewamalage and Yuanhao Liu and Yanfei Kang and C. Bergmeir},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.02663}
}
  • Kasun Bandara, Hansika Hewamalage, +2 authors C. Bergmeir
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many… CONTINUE READING

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