论文标题
Dynaconf：非平稳时间序列的动态预测
DynaConF: Dynamic Forecasting of Non-Stationary Time Series
论文作者
论文摘要
深度学习在各种时间序列预测任务中都表现出了令人印象深刻的结果，在这些任务中，鉴于过去的未来有条件分布是本质的。但是，当此条件分布是非平稳的时候，这些模型始终学习并准确预测的挑战。在这项工作中，我们提出了一种新方法，通过清楚地将固定的条件分布模型从非平稳动力学建模中清晰地取消固定的条件分布建模来对非平稳条件分布进行建模。我们的方法基于一个贝叶斯动态模型，该模型可以适应条件分布的变化和深层条件分布模型，该模型使用分解的输出空间处理多元时间序列。我们对合成和现实世界数据集的实验结果表明，我们的模型可以比最先进的深度学习解决方案更好地适应非平稳时间序列。
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.
