Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, ...
People also ask
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 best statistical model for time series forecasting?
AutoRegressive Integrated Moving Average (ARIMA) models are among the most widely used time series forecasting techniques: In an Autoregressive model, the forecasts correspond to a linear combination of past values of the variable.
This is the public repo for the paper "Robust Probabilistic Time Series Forecasting" (AISTATS '22). Requirements. Recent versions of GluonTS, PyTorch, and ...
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, ...
Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
PROFHiT: Probabilistic Robust Forecasting for Hierarchical Time-series. Setup. Make sure anaconda or miniconda is installed. Pachake required are listed in ...
[PDF] Robust Probabilistic Time Series Forecasting | Semantic Scholar
www.semanticscholar.org › paper
This work generalizes the concept of adversarial input perturbations, based on which the idea of robustness is formulated in terms of bounded Wasserstein ...
Jul 19, 2022 · Abstract:This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
Mar 28, 2022 · In this work, we propose the framework of robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...