Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting

no code implementations • 16 Nov 2023 • Romain Ilbert, Thai V. Hoang, Zonghua Zhang, Themis Palpanas

Our optimal model can retain up to $92. 02\%$ the performance of the original forecasting model in terms of Mean Squared Error (MSE) on clean data, while being more robust than the standard adversarially trained models on perturbed data.

Computational Efficiency Time Series Forecasting

Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

no code implementations • 25 Jan 2022 • Hamza Bodor, Thai V. Hoang, Zonghua Zhang

Generally, unsupervised anomaly detection algorithms gain more popularity than the supervised ones, due to the fact that labeling KPIs is extremely time- and resource-consuming, and error-prone.

Active Learning Time Series +2