4 papers
Contrastive Time Series Forecasting with Anomalies
Joel Ekstrand, Zahra Taghiyarrenani, Slawomir Nowaczyk
Time series forecasting predicts future values from past data. In real-world settings, some anomalous events have lasting effects and influence the forecast, while others are short…
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
Joel Ekstrand, Tor Mattsson, Zahra Taghiyarrenani +3
Reliable forecasting of multivariate time series under anomalous conditions is crucial in applications such as ATM cash logistics, where sudden demand shifts can disrupt operations…
Intrusion Detection in Heterogeneous Networks with Domain-Adaptive Multi-Modal Learning
Mabin Umman Varghese, Zahra Taghiyarrenani
Network Intrusion Detection Systems (NIDS) play a crucial role in safeguarding network infrastructure against cyberattacks. As the prevalence and sophistication of these attacks in…
Heterogeneous Federated Learning via Personalized Generative Networks
Zahra Taghiyarrenani, Abdallah Alabdallah, Slawomir Nowaczyk +1
Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistic…