Summary of Large Language Models For Financial Aid in Financial Time-series Forecasting, by Md Khairul Islam et al.
Large Language Models for Financial Aid in Financial Time-series Forecasting
by Md Khairul Islam, Ayush Karmacharya, Timothy Sue, Judy Fox
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Financial time series forecasting is a challenging task in financial aid due to limited historical datasets and high-dimensional financial information. Current research focuses on leveraging big data analytics to develop effective predictive models that balance accuracy with efficient runtime and memory usage. This paper employs pre-trained foundation models, including GPT-2 as the backbone, transformers, and linear models, to demonstrate their ability to outperform traditional approaches in scarce financial datasets. The study uses a benchmark of eight time series tasks, including financial aid, to show the potential of using language models for forecasting financial trends. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about predicting future events in finance, like how much money people will get or need. It’s hard because there isn’t much information from the past and it’s complicated. The researchers used special tools called “predictive analysis” to help make better predictions. They tested these tools on different types of data and found that they worked well even when not much training was needed. This is important for financial aid, which helps people pay for things like college or healthcare. |
Keywords
» Artificial intelligence » Gpt » Time series
