  Multivariate time series (MTS) regression tasks are common in many real-world
data mining applications including finance, cybersecurity, energy, healthcare,
prognostics, and many others. Due to the tremendous success of deep learning
(DL) algorithms in various domains including image recognition and computer
vision, researchers started adopting these techniques for solving MTS data
mining problems, many of which are targeted for safety-critical and
cost-critical applications. Unfortunately, DL algorithms are known for their
susceptibility to adversarial examples which also makes the DL regression
models for MTS forecasting also vulnerable to those attacks. To the best of our
knowledge, no previous work has explored the vulnerability of DL MTS regression
models to adversarial time series examples, which is an important step,
specifically when the forecasting from such models is used in safety-critical
and cost-critical applications. In this work, we leverage existing adversarial
attack generation techniques from the image classification domain and craft
adversarial multivariate time series examples for three state-of-the-art deep
learning regression models, specifically Convolutional Neural Network (CNN),
Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our
study using Google stock and household power consumption dataset. The obtained
results show that all the evaluated DL regression models are vulnerable to
adversarial attacks, transferable, and thus can lead to catastrophic
consequences in safety-critical and cost-critical domains, such as energy and
finance.
