ESROP – KMUTT – Time series Data Modeling and Analysis with a focus on financial data

This project focuses on improving transformer-based models for quantitative finance (QFin) by addressing key limitations such as inadequate long-term memory, lack of sentiment analysis, and difficulties in handling multimodal data. While transformers have achieved breakthroughs in fields like protein folding and natural language processing, their application in finance still faces challenges. Existing financial transformers struggle with capturing long-term dependencies, relying mainly on structured technical data while neglecting external factors like news and market sentiment.

This research aims to enhance financial transformers by integrating better memory mechanisms, sentiment analysis techniques from NLP models like BERT and T5, and multimodal learning to improve predictive accuracy and adaptability in volatile market conditions.

Faculty Supervisor:

Arthur Chan

Student:

Partner:

King Mongkut’s University of Technology Thonburi

Discipline:

Engineering

Sector:

Artificial Intelligence; Finance and Insurance; Information and Communications Technology

University:

University of Toronto

Program: