INTARG generates effective real-time adversarial attacks on time-series regression models by selectively targeting high-confidence high-error steps in a bounded-buffer online setting, increasing prediction error up to 2.42x while attacking under 10% of timesteps.
A comprehensive review on deep learning approaches for short-term load forecasting
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GCA-BULF is a bottom-up STLF framework that filters and groups critical appliances for collaborative forecasting, reporting 20.85-57.88% better hourly accuracy than top-down methods on residential and office data.
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INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
INTARG generates effective real-time adversarial attacks on time-series regression models by selectively targeting high-confidence high-error steps in a bounded-buffer online setting, increasing prediction error up to 2.42x while attacking under 10% of timesteps.
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GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances
GCA-BULF is a bottom-up STLF framework that filters and groups critical appliances for collaborative forecasting, reporting 20.85-57.88% better hourly accuracy than top-down methods on residential and office data.