Large Language Models for Forecasting and Anomaly Detection: A
  Systematic Literature Review
Jing Su Chufeng Jiang Xin Jin Yuxin Qiao Tingsong Xiao Hongda Ma Rong Wei Zhi Jing Jiajun Xu Junhong Lin
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, issues with
generalizability across different contexts, the phenomenon of model
hallucinations, limitations within the models' knowledge boundaries, and the
substantial computational resources required. Through detailed analysis, this
review discusses potential solutions and strategies to overcome these
obstacles, such as integrating multimodal data, advancements in learning
methodologies, and emphasizing model explainability and computational
efficiency. Moreover, this review outlines critical trends that are likely to
shape the evolution of LLMs in these fields, including the push toward
real-time processing, the importance of sustainable modeling practices, and the
value of interdisciplinary collaboration. Conclusively, this review underscores
the transformative impact LLMs could have on forecasting and anomaly detection
while emphasizing the need for continuous innovation, ethical considerations,
and practical solutions to realize their full potential.