DynaConF: Dynamic Forecasting of Non-Stationary Time Series
Abstract: Deep learning has 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 accurately. In this work, we propose a new method to model non-stationary conditional distributions over time by clearly decoupling stationary conditional distribution modeling from non-stationary dynamics modeling. Our method is based on a Bayesian dynamic model that can adapt to conditional distribution changes and a deep conditional distribution model that handles multivariate time series using a factorized output space. Our experimental results on synthetic and real-world datasets show that our model can adapt to non-stationary time series better than state-of-the-art deep learning solutions.
Submission Length: Regular submission (no more than 12 pages of main content)
Code: https://github.com/BorealisAI/dcf
Assigned Action Editor: ~Fredrik_Daniel_Johansson1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 1746
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