DynaConF: Dynamic Forecasting of Non-Stationary Time-Series

@article{Liu2022DynaConFDF,
  title={DynaConF: Dynamic Forecasting of Non-Stationary Time-Series},
  author={Siqi Liu and Andreas M. Lehrmann},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.08411}
}
Deep learning models have shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. However, when this conditional distribution is non-stationary, it poses challenges for these models to learn consistently and to predict accu-rately. In this work, we propose a new method to model non-stationary conditional distributions over time by clearly decoupling stationary conditional distribution… 
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