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However, these independent functions fail to capture correlations among multiple time series, thereby reducing prediction accuracy. Moreover, ex- isting robust ...

Mar 21, 2022 · Time series forecasting is a set of methods in statistics and data science to predict some variables that develop and change over time.

hyperparmeter samples for FL model is noticeably large (i.e., the robustness of the model and the prevalent advantage of using probabilistic model are not ...

This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.

Apr 1, 2022 · Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series observations.

type of model robustness for time-series forecasting on adversarial noises ... Robust probabilistic time series forecasting. arXiv preprint arXiv ...

Jul 19, 2022 · This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.

Feb 11, 2024 · Instead of using the entire history of each time series for prediction, a fixed context window is used to learn an approximation of the ...

Apr 12, 2024 · Approach: We utilize GluonTS, a deep learning framework emphasizing probabilistic forecasting, to build robust models capable of handling the ...