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 ...
Apr 14, 2023 · 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 ...
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
This assumption is unrealistic since the collected time series data can be contaminated in practice. The forecasting model will be inferior if it is directly ...
Both methods may lose important knowledge from the original data. In this paper, we propose a multidimensional time series forecasting framework that can better ...
Apr 4, 2024 · Based on this analysis, the forecasting solution might predict a deterministic demand of 10,000 units for the next quarter using a weighted ...