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 3, 2024 · In this paper, we first statistically define three types of anomalies, then theoretically and experimentally analyze the loss robustness and ...
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
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 ...
forecast: Forecasting Functions for Time Series and Linear Models. R Package Version 8.2. [Online]. Available: http://pkg. robjhyndman.com/forecast. [38] ...
Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...