Large Language Models for Financial Aid in Financial Time-series Forecasting
This addresses forecasting challenges for financial aid applications with scarce datasets, though it is incremental as it applies existing LLMs to a new domain.
The paper tackles financial time series forecasting with limited historical data by using pre-trained large language models (LLMs) like GPT-2, showing they outperform traditional methods in few-shot or zero-shot settings on financial aid and other tasks.
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.