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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.10350 |
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| _version_ | 1866917591207903232 |
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| author | Su, Jing Jiang, Chufeng Jin, Xin Qiao, Yuxin Xiao, Tingsong Ma, Hongda Wei, Rong Jing, Zhi Xu, Jiajun Lin, Junhong |
| author_facet | Su, Jing Jiang, Chufeng Jin, Xin Qiao, Yuxin Xiao, Tingsong Ma, Hongda Wei, Rong Jing, Zhi Xu, Jiajun Lin, Junhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10350 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review Su, Jing Jiang, Chufeng Jin, Xin Qiao, Yuxin Xiao, Tingsong Ma, Hongda Wei, Rong Jing, Zhi Xu, Jiajun Lin, Junhong Machine Learning Artificial Intelligence 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. |
| title | Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.10350 |