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Title: Large Language Models for Financial Aid in Financial Time-series Forecasting
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.  more » « less
Award ID(s):
2151597 1918626
PAR ID:
10583583
Author(s) / Creator(s):
; ; ;
Corporate Creator(s):
Editor(s):
na
Publisher / Repository:
IEEE Computer Society Digital Library
Date Published:
Edition / Version:
1
ISBN:
979-8-3503-6248-0
Page Range / eLocation ID:
4892 to 4895
Subject(s) / Keyword(s):
Financial Aid Time Series Forecast Deep Learning, Foundation Models Large Language Models
Format(s):
Medium: X Other: pdf
Location:
Washington, DC, USA
Sponsoring Org:
National Science Foundation
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