Large Language Models for Financial Aid in Financial Time-series Forecasting

• URL: http://arxiv.org/abs/2410.19025v1
• Date: Thu, 24 Oct 2024 12:41:47 GMT
• Title: Large Language Models for Financial Aid in Financial Time-series Forecasting
• Authors: Md Khairul Islam, Ayush Karmacharya, Timothy Sue, Judy Fox,
• Abstract summary: Time series forecasting in financial aid is difficult due to limited historical datasets and high dimensional financial information. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as the backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches.
• Score: 0.4218593777811082
• License:
• Abstract: Considering the difficulty of financial time series forecasting in financial aid, much of the current research focuses on leveraging big data analytics in financial services. One modern approach is to utilize "predictive analysis", analogous to forecasting financial trends. However, many of these time series data in Financial Aid (FA) pose unique challenges due to limited historical datasets and high dimensional financial information, which hinder the development of effective predictive models that balance accuracy with efficient runtime and memory usage. Pre-trained foundation models are employed to address these challenging tasks. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as the backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches, even with minimal ("few-shot") or no fine-tuning ("zero-shot"). Our benchmark study, which includes financial aid with seven other time series tasks, shows the potential of using LLMs for scarce financial datasets.

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