Robust Probabilistic Time Series Forecasting

Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:1336-1358, 2022.

Abstract

Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, based on which we formulate the concept of robustness in terms of bounded Wasserstein deviation. Then we extend the randomized smoothing technique to attain robust probabilistic forecasters with theoretical robustness certificates against certain classes of adversarial perturbations. Lastly, extensive experiments demonstrate that our methods are empirically effective in enhancing the forecast quality under additive adversarial attacks and forecast consistency under supplement of noisy observations. The code for our experiments is available at https://github.com/tetrzim/robust-probabilistic-forecasting.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-yoon22a, title = { Robust Probabilistic Time Series Forecasting }, author = {Yoon, Taeho and Park, Youngsuk and Ryu, Ernest K. and Wang, Yuyang}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {1336--1358}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/yoon22a/yoon22a.pdf}, url = {https://proceedings.mlr.press/v151/yoon22a.html}, abstract = { Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, based on which we formulate the concept of robustness in terms of bounded Wasserstein deviation. Then we extend the randomized smoothing technique to attain robust probabilistic forecasters with theoretical robustness certificates against certain classes of adversarial perturbations. Lastly, extensive experiments demonstrate that our methods are empirically effective in enhancing the forecast quality under additive adversarial attacks and forecast consistency under supplement of noisy observations. The code for our experiments is available at https://github.com/tetrzim/robust-probabilistic-forecasting. } }
Endnote
%0 Conference Paper %T Robust Probabilistic Time Series Forecasting %A Taeho Yoon %A Youngsuk Park %A Ernest K. Ryu %A Yuyang Wang %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-yoon22a %I PMLR %P 1336--1358 %U https://proceedings.mlr.press/v151/yoon22a.html %V 151 %X Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, based on which we formulate the concept of robustness in terms of bounded Wasserstein deviation. Then we extend the randomized smoothing technique to attain robust probabilistic forecasters with theoretical robustness certificates against certain classes of adversarial perturbations. Lastly, extensive experiments demonstrate that our methods are empirically effective in enhancing the forecast quality under additive adversarial attacks and forecast consistency under supplement of noisy observations. The code for our experiments is available at https://github.com/tetrzim/robust-probabilistic-forecasting.
APA
Yoon, T., Park, Y., Ryu, E.K. & Wang, Y.. (2022). Robust Probabilistic Time Series Forecasting . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:1336-1358 Available from https://proceedings.mlr.press/v151/yoon22a.html.

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