Adversarial attacks against Bayesian forecasting dynamic models
This work tackles the problem of securing forecasting models for users in time series applications, but it appears incremental as it extends existing adversarial methods to a new domain.
The paper addresses the lack of adversarial attacks on time series forecasting systems by proposing a decision analysis-based strategy against Bayesian forecasting dynamic models, though no specific results or numbers are provided.
The last decade has seen the rise of Adversarial Machine Learning (AML). This discipline studies how to manipulate data to fool inference engines, and how to protect those systems against such manipulation attacks. Extensive work on attacks against regression and classification systems is available, while little attention has been paid to attacks against time series forecasting systems. In this paper, we propose a decision analysis based attacking strategy that could be utilized against Bayesian forecasting dynamic models.