3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.AI2025
Architectures for Building Agentic AI
SÅawomir Nowaczyk
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers o…