Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

arXiv:2402.10350v1 Announce Type: new
Abstract: This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, …

abstract anomaly anomaly detection application arxiv challenges cs.ai cs.lg current datasets detection forecasting future highlighting identify language language models large language large language models literature llms parsing research review state type