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This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data.
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Language models are few-shot learners
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Time-llm: Time series forecasting by reprogramming large language models
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Reflexion: an autonomous agent with dynamic memory and self-reflection
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Test: Text prototype aligned embedding to activate llm’s ability for time series
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Llama: Open and efficient foundation language models
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Llama 2: Open foundation and fine-tuned chat models
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Toward a foundation model for time series data
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A survey of large language models
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One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al · 2023
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News and load: A quantitative exploration of natural language processing applications for forecasting day-ahead electricity system demand
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