In the present work, we investigate robust probabilistic forecasting models which aim to satisfy the both requirements. In the classical time series literature ...
This is the public repo for the paper "Robust Probabilistic Time Series Forecasting" (AISTATS '22). ... An example command line input. python train.py ...
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In this work, we propose a framework for robust probabilistic time series forecasting. ... example, determining how. many units of each item to purchase and ...
When outliers are present in a dataset, they can disrupt the calculated summary statistics, such as the mean and standard deviation, leading the model to ...
Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
Apr 4, 2024 · Probabilistic forecasting is best suited for dynamic and uncertain environments, medium- to long-term strategic forecasts, and data-rich ...
Jan 16, 2024 · Thus it is essential to develop methods to automatically learn a robust forecasting model from the contaminated data. In this paper, we first ...
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
Apr 17, 2017 · Quantification of forecast uncertainty via simulation-based prediction intervals. We conclude with an example of our forecasting routine applied ...
Apr 14, 2023 · This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.