Abstract
Time Series Forecasting (TSF) is well established in domains dealing with temporal data to predict future events yielding the basis for strategic decision-making. Previous research indicated that forecasting models are vulnerable to adversarial attacks, that is, maliciously crafted perturbations of the original data with the goal of altering the model’s predictions. However, attackers targeting specific outcomes pose a substantially more severe threat as they could manipulate the model and bend it to their needs. Regardless, there is no systematic approach for targeted adversarial learning in the TSF domain yet. In this paper, we introduce targeted attacks on TSF in a systematic manner. We establish a new experimental design standard regarding attack goals and perturbation control for targeted adversarial learning on TSF. For this purpose, we present a novel indirect sparse black-box evasion attack on TSF, n Vita. Additionally, we adapt the popular white-box attacks Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). Our experiments confirm not only that all three methods are effective but also that current state-of-the-art TSF models are indeed susceptible to attacks. These results motivate future research in this area to achieve higher reliability of forecasting models.
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Notes
- 1.
All materials, including Python implementation, supplementary materials, and additional results, are available at https://github.com/ProfiterolePuff/nvita.
- 2.
Source: https://open-power-system-data.org/.
- 3.
Source: https://finance.yahoo.com/.
References
Biggio, B., Fumera, G., Roli, F.: Multiple classifier systems for robust classifier design in adversarial environments. J. Mach. Learn. Cybern. 1, 27–41 (2010)
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: ICML, pp. 1613–1622. PMLR (2015)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)
Cowtan, K.: The climate data guide: Global surface temperatures: berkeley earth surface temperatures (2019). https://bit.ly/3fAqtVg Accessed 18 Feb 2022
Dalvi, N., Domingos, P., Sanghai, S., Verma, D.: Adversarial classification. In: The tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 99–108 (2004)
Dang-Nhu, R., Singh, G., Bielik, P., Vechev, M.: Adversarial attacks on probabilistic autoregressive forecasting models. In: III, H.D., Singh, A. (eds.) The 37th ICML. vol. 119, pp. 2356–2365. PMLR, 13–18 Jul 2020
Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924 (2017)
Demontis, A., et al.: Yes, machine learning can be more secure! A case study on android malware detection. IEEE Trans. Dependable Secure Comput. 16(4), 711–724 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artif. Intell. Rev. 53(8), 5929–5955 (2020). https://doi.org/10.1007/s10462-020-09838-1
Kołcz, A., Teo, C.H.: Feature weighting for improved classifier robustness. In: CEAS ’09, Mountain View, CA, USA (2009)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. IEEE 86(11), 2278–2324 (1998)
Liu, L., Park, Y., Hoang, T.N., Hasson, H., Huan, J.: Towards robust multivariate time-series forecasting: adversarial attacks and defense mechanisms. In: KDD 2022 Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications (2022)
Mathieu, E., et al.: Coronavirus pandemic (covid-19). Our World in Data (2020). https://ourworldindata.org/coronavirus
Mode, G.R., Hoque, K.A.: Adversarial examples in deep learning for multivariate time series regression. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–10. IEEE (2020)
Mondal, P., Shit, L., Goswami, S.: Study of effectiveness of time series modeling (arima) in forecasting stock prices. IJCSEA 4(2), 13 (2014)
Razvan-Gabriel Cirstea, Chenjuan Guo, B.Y.: Graph attention recurrent neural networks for correlated time series forecasting. In: KDD MiLeTS19 (2019)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Storn, R., Price, K.: Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. J. Global Optim. 23 (1995)
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828–841 (2019)
Wu, T., Wang, X., Qiao, S., Xian, X., Liu, Y., Zhang, L.: Small perturbations are enough: Adversarial attacks on time series prediction. Inf. Sci. 587, 794–812 (2022)
Xu, A., Wang, X., Zhang, Y., Wu, T., Xian, X.: Adversarial attacks on deep neural networks for time series prediction. In: 2021 10th ICICSE, pp. 8–14 (2021)
Yoon, Y., Swales, G.: Predicting stock price performance: a neural network approach. In: The Twenty-Fourth Annual Hawaii International Conference on System Sciences, vol. 4, pp. 156–162 (1991)
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)
Zhang, X., et al.: Traffic flow forecasting with spatial-temporal graph diffusion network. In: The AAAI Conference on Artificial Intelligence, vol. 35, pp. 15008–15015 (2021)
Zhang, Z.: Multivariate Time Series Analysis in Climate and Environmental Research. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67340-0
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Chen, Z., Dost, K., Zhu, X., Chang, X., Dobbie, G., Wicker, J. (2023). Targeted Attacks on Time Series Forecasting. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13938. Springer, Cham. https://doi.org/10.1007/978-3-031-33383-5_25
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