Large Language Models (LLMs) have gained prominence for their ability to handle complex data and extract meaningful insights. This study investigates the effectiveness of LLMs in time series data analysis, a critical task across domains like healthcare, energy, and finance. Time series analysis involves tasks such as classification, anomaly detection, and forecasting, which are essential for informed decision-making. The research compares LLM-based methods with non-LLM approaches across these tasks, using models like GPT4TS and autoregressive models. The findings indicate that while LLMs excel in specific tasks like anomaly detection, their benefits are less pronounced in others, such as forecasting, where simpler models sometimes perform comparably or better. This highlights the potential of LLMs in time series analysis while also pointing out their limitations. The study lays the groundwork for future research to systematically explore the applications and limitations of LLMs in handling temporal data, offering insights into their role in enhancing decision-making processes.
GPT4TS, Autoregressive Models
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Benchmark datasets
Accuracy, Precision
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No
No
Handles complex data, excels in anomaly detection
No
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No
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Less effective in forecasting compared to simpler models
Healthcare, Energy, Finance
Time series analysis, Anomaly detection
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No
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No
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No
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0.00
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01/01/1970
01/01/1970
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Yes