TraceLM: Temporal Root-Cause Analysis with Contextual Embedding Language Models

Temporal analysis represents a crucial challenge in understanding event sequences, particularly in identifying root causes. Traditional methods often rely on static feature extraction, limiting their effectiveness in dynamic contexts. To tackle this issue, we introduce TraceLM, a framework that employs contextual embedding language models to enhance temporal root-cause analysis. By incorporating advanced representation learning techniques, TraceLM captures the temporal dynamics and relationships inherent in data. The multi-layered architecture allows for the identification of significant patterns indicative of root causes by processing sequences dynamically. Evaluations on benchmark datasets reveal that TraceLM outperforms existing methods in both accuracy and efficiency, demonstrating its strength in uncovering complex causative relationships. This capability provides actionable insights relevant to real-world domains such as system diagnostics and incident management, thus significantly advancing the field of root-cause analysis.