Robust Probabilistic Time Series Forecasting
This work addresses robustness in probabilistic forecasting for decision-making applications, but it is incremental as it adapts existing techniques to a new domain.
The paper tackles the problem of deep probabilistic time series forecasting models being vulnerable to input perturbations by proposing a framework that generalizes adversarial perturbations and uses randomized smoothing to achieve robust forecasters with theoretical certificates, demonstrating effectiveness in improving forecast quality under attacks and consistency with noisy data.
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, based on which we formulate the concept of robustness in terms of bounded Wasserstein deviation. Then we extend the randomized smoothing technique to attain robust probabilistic forecasters with theoretical robustness certificates against certain classes of adversarial perturbations. Lastly, extensive experiments demonstrate that our methods are empirically effective in enhancing the forecast quality under additive adversarial attacks and forecast consistency under supplement of noisy observations.