Corpus ID: 218719540

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models

@article{Rabanser2020TheEO,
  title={The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models},
  author={Stephan Rabanser and Tim Januschowski and Valentin Flunkert and David Salinas and Jan Gasthaus},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.10111}
}
Time series modeling techniques based on deep learning have seen many advancements in recent years, especially in data-abundant settings and with the central aim of learning global models that can extract patterns across multiple time series. While the crucial importance of appropriate data pre-processing and scaling has often been noted in prior work, most studies focus on improving model architectures. In this paper we empirically investigate the effect of data input and output… Expand
Forecasting: theory and practice.
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series

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