Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
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In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, ...
In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, ...
This is the public repo for the paper "Robust Probabilistic Time Series Forecasting" (AISTATS '22). Requirements. Recent versions of GluonTS, PyTorch, and ...
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What is probabilistic time series forecasting?
Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.
What is robust forecasting?
We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks.
Which method is best for time series forecasting?
The time series method of forecasting involves analyzing historical data points collected over time to identify patterns and trends. By applying statistical techniques and models, such as ARIMA, Exponential Smoothing, or Seasonal Decomposition, it predicts future values based on these identified patterns.
What is the probabilistic model of forecasting?
A probabilistic forecast contains a set of probabilities associated with all possible future outcomes instead of just one particular outcome being pinpointed across the forecast.
In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations ...
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Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
Mar 28, 2022 · In this work, we propose the framework of robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
Jan 16, 2024 · Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model.
3 Shape and time diversity for probabilistic time series forecasting ... Robust estimation, prediction and control with linear dynamics and generic costs.
Oct 17, 2023 · Probabilistic hierarchical time-series forecasting is an important variant oftime-series forecasting, where the goal is to model and ...
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