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Certified robust- ness to adversarial examples with ... A neural network ensemble method with jittered training data for time series forecasting.
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 ...
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We present STRIPE, a probabilistic time series forecasting method that introduces structured shape and temporal diversity based on determinantal point processes ...
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[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 ...
This forecasting model is based on adversarial domain adaptation and includes two novel modules: Correlated. Robust Forecaster (CORF) and Domain Critic.
We present STRIPE, a probabilistic time series forecasting method that introduces structured shape and temporal diversity based on determinantal point processes ...
Feb 24, 2022 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input ...
Figure 1: Example predictions on exchange rate (left), ETTm2 (a sequence of electricity transformer temperature readings, center), and weather (right) for NHiTS ...
RegHD encodes time series data into a binary hypervector which is used to forecast the next value. The encoding and forecasting operations are simple thus save ...