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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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 the robust approach to 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?
ARIMA and SARIMA
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.
What is an example of a probabilistic forecast?
For example, "tonight's low will be 31 degrees Fahrenheit," or "0.46 inches of rain will fall tomorrow." A probabilistic forecast is one in which forecasters convey uncertainties by expressing forecasts as probabilities of various outcomes.
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 ...
Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
[PDF] Robust Probabilistic Time Series Forecasting | Semantic Scholar
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This work generalizes the concept of adversarial input perturbations, based on which the idea of robustness is formulated in terms of bounded Wasserstein ...
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. 4. Paper · Code ...
Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly ...
The hybrid model is robust for forecasting time series due to its ability to handle input perturbations and ensure forecast quality and consistency against ...
Mar 28, 2022 · In this work, we propose the framework of robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
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