Multivariate time seiers forecasting by using graph neural networks with applications to financial technology

The objective of this project is to develop an AI-powered Fintech system that can analyze multivariate financial data. The system aims to tap into global markets, with a focus on Hong Kong technology. The research and development methodology involved in this project includes the use of innovative graph neural network technologies, namely Time Series Attention Transformers and Super-Empirical Mode Decomposition. These technologies will enable the integration of data from multiple time series and facilitate financial analysis.

The project will have two main applications. The first application is currency trading analysis, which will leverage the AI-powered system to analyze and predict currency trends and fluctuations. This will provide valuable insights for traders and investors in the foreign exchange market. The second application is agricultural commodity analysis, where the system will analyze various factors hidden in the prices of different agricultural products to provide accurate predictions and analysis for agricultural commodity trading.

The impact and benefits of this project are significant. By leveraging AI and advanced data analysis techniques, the system will provide more accurate and timely financial analysis, enabling traders and investors to make informed decisions. This will not only enhance profitability but also reduce risks associated with financial trading. Furthermore, the project will contribute to the growth and development of the Fintech industry in Hong Kong, positioning it as a global leader in this field. Overall, this project will have a positive impact on the financial sector and the economy as a whole.