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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