Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting

• URL: http://arxiv.org/abs/2503.23190v1
• Date: Sat, 29 Mar 2025 19:04:28 GMT
• Title: Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting
• Authors: Eftychia Makri, Georgios Palaiokrassas, Sarah Bouraga, Antigoni Polychroniadou, Leandros Tassiulas,
• Abstract summary: This paper presents a study on the effectiveness of Large Language Models (LLMs) in predicting prices for short-term and few-shot forecasting scenarios.<n>We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of price time series data.<n>This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE)
• Score: 13.40649684167945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem. This paper presents a comprehensive study on the effectiveness of Large Language Models (LLMs) in predicting Ethereum prices for short-term and few-shot forecasting scenarios. The main challenge in training models for time series analysis is the lack of data. We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of Ethereum price time series data. Through thorough experimentation and comparison with traditional and contemporary models, our results demonstrate that selectively freezing certain layers of pre-trained LLMs achieves state-of-the-art performance in this domain. This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), demonstrating its effectiveness and robustness. Our research not only contributes to the existing body of knowledge on LLMs but also provides practical insights in the cryptocurrency prediction domain. The adaptability of pre-trained LLMs to handle the nature of Ethereum prices suggests a promising direction for future research, potentially including the integration of sentiment analysis to further refine forecasting accuracy.

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