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Certified robust- ness to adversarial examples with ... A neural network ensemble method with jittered training data for time series forecasting.
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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.
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 an example of time series forecasting?
Examples of time series forecasting
Forecasting the closing price of a stock each day. Forecasting product sales in units sold each day for a store. Forecasting unemployment for a state each quarter. Forecasting the average price of gasoline each day.
What is the probabilistic forecasting model?
As discussed in the previous section, the aim of a probabilistic forecast is to estimate the future distribution of the demand whether at a single time step (univariate) or multiple (multivariate). To estimate the uncertainty requires accurately modelling the variation.
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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This is the public repo for the paper "Robust Probabilistic Time Series Forecasting" (AISTATS '22). ... An example command line input. python train.py ...
In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, ...
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. 4. Paper · Code ...
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Sep 28, 2023 · In this post, we train a robust time series forecasting model capable of capturing such extreme events using Amazon SageMaker. To ...
In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, ...
[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 ...
Metareview: This paper considers more realistic settings for time series forecasting in which different types of anomalies exist in the training data. It aims ...
We present STRIPE, a probabilistic time series forecasting method that introduces structured shape and temporal diversity based on determinantal point processes ...