  Adversarial training is a method for enhancing neural networks to improve the
robustness against adversarial examples. Besides the security concerns of
potential adversarial examples, adversarial training can also improve the
generalization ability of neural networks, train robust neural networks, and
provide interpretability for neural networks. In this work, we introduce
adversarial training in time series analysis to enhance the neural networks for
better generalization ability by taking the finance field as an example.
Rethinking existing research on adversarial training, we propose the adaptively
scaled adversarial training (ASAT) in time series analysis, by rescaling data
at different time slots with adaptive scales. Experimental results show that
the proposed ASAT can improve both the generalization ability and the
adversarial robustness of neural networks compared to the baselines. Compared
to the traditional adversarial training algorithm, ASAT can achieve better
generalization ability and similar adversarial robustness.
