DynaConF: Dynamic Forecasting of Non-Stationary Time Series
- URL: http://arxiv.org/abs/2209.08411v3
- Date: Sat, 24 Feb 2024 17:47:11 GMT
- Title: DynaConF: Dynamic Forecasting of Non-Stationary Time Series
- Authors: Siqi Liu, Andreas Lehrmann
- Abstract summary: We propose a new method to model non-stationary conditional distributions over time.
We show that our model can adapt to non-stationary time series better than state-of-the-art deep learning solutions.
- Score: 4.286546152336783
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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