Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
(2402.10350)Abstract
This systematic literature review comprehensively examines the application of 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.
Overview
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LLMs are pivotal in advancing forecasting and anomaly detection, analyzing extensive datasets to predict future events and identify anomalies accurately.
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LLMs are used in diverse applications such as time series forecasting, event sequence prediction, and healthcare clinical prediction, demonstrating their ability to process and analyze vast amounts of data.
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Challenges in LLM adoption include dependency on large historical datasets, data quality issues, model generalizability, hallucinations, and concerns about robustness and efficiency.
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Future directions include integrating multimodal data, advancements in transfer and meta-learning, emphasis on model explainability, and sustainable modeling practices.
Leveraging LLMs for Forecasting and Anomaly Detection: A Comprehensive Review
LLMs have increasingly become central to advancements in forecasting and anomaly detection across various domains. These models, which have initially been honed for a vast array of natural language processing tasks, are now being applied to parse extensive datasets, predict future events, and pinpoint anomalies with significant accuracy. This systematic literature review explore the current state of LLM applications in these areas, shedding light on methodologies, inherent challenges, and the promising horizon that lies ahead.
Current Methodologies and Applications
LLMs offer a robust framework for understanding and generating predictions based on historical data. In areas like time series forecasting, event sequence prediction, traffic flow forecasting, and healthcare clinical prediction, LLMs have shown their prowess. These applications leverage LLMs' ability to process and analyze massive amounts of data, identifying patterns and deviations that might elude traditional analysis methods.
Challenges Facing LLM Adoption
Despite their potential, the deployment of LLMs in forecasting and anomaly detection faces several hurdles. A significant dependency on extensive historical datasets presents challenges in data availability, quality, and inherent biases. Moreover, ensuring the generalizability of these models across diverse contexts remains a formidable task. Issues such as model hallucinations, where LLMs generate plausible but incorrect or misleading information, along with concerns over the robustness and computational efficiency of these models, further complicate their widespread adoption.
The Road Ahead: Future Directions
Emerging trends promise to address these challenges, broadening the scope and enhancing the performance of LLMs in forecasting and anomaly detection. Notably, the integration of multimodal data sources and advancements in transfer and meta-learning are poised to improve model adaptability and learning efficiency. Emphasis on model explainability and the push towards real-time processing underscore the growing need for LLMs that are not only accurate but are also transparent and capable of operating in dynamic environments. Furthermore, sustainable modeling practices highlight an increasing awareness of the environmental and ethical considerations surrounding LLM deployment.
Conclusion
The application of LLMs in forecasting and anomaly detection offers a glimpse into a future where predictive analytics is more sophisticated, accurate, and impactful. While challenges remain, the pathways to overcoming these obstacles are becoming increasingly clear, thanks to ongoing research and technological innovations. As we move forward, it is imperative that the development and deployment of LLMs continue to be guided by principles of ethical consideration, ensuring that these advances benefit society as a whole.
This comprehensive review underscores the transformative potential of LLMs in forecasting and anomaly detection, marking a significant step toward harnessing the full capabilities of advanced computational models to navigate the complexities of the modern world.
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