Adversarial Attacks on Deep Neural Networks for Time Series Prediction

Author:

Xu Aidong1,Wang Xuechun2,Zhang Yunan1,Wu Tao3,Xian Xingping3

Affiliation:

1. Guangdong Provincial Key Laboratory of Power System Network Security, Electric Power Research Institute, CSG, China

2. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China

3. School of Cybersecurity and Information Law, Chongqing University of Posts and Telecommunications, China

Publisher

ACM

Reference25 articles.

1. Time-series data mining

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3. A Deep Hybrid Model for Weather Forecasting

4. Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach

5. Bedi J , Toshniwal D. 2019. Deep learning framework to forecast electricity demand. Applied energy, 238, (March 2019 ), 1312-1326. https://doi.org/10.1016/j.apenergy.2019.01.113. 10.1016/j.apenergy.2019.01.113 Bedi J, Toshniwal D. 2019. Deep learning framework to forecast electricity demand. Applied energy, 238, (March 2019), 1312-1326. https://doi.org/10.1016/j.apenergy.2019.01.113.

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