Projects
To be continued...
2024
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FAST-CA: Fusion-based Adaptive Spatial-Temporal Learning with Coupled Attention for airport network delay propagation predictionChi Li , Xixian Qi , Yuzhe Yang , and 3 more authorsInformation Fusion, 2024The issue of delay propagation prediction in airport networks has garnered increasing global attention, particularly due to its profound impact on operational efficiency and passenger satisfaction in modern air transportation systems. Despite research advancements in this domain, existing methodologies often fall short of comprehensively addressing the challenges associated with predicting delay propagation in airport networks, especially in terms of handling complex spatial-temporal dependencies and sequence couplings. In response to the complex challenge of predicting delay propagation in airport networks, we introduce the Fusion-based Adaptive Spatial-Temporal Learning with Coupled Attention (FAST-CA) framework. FAST-CA is an innovative model that integrates dynamic and adaptive graph learning, coupled attention mechanisms, periodicity feature extraction, and multifaceted information fusion modules. This holistic approach enables a thorough analysis of the interplay between flight departure and arrival delays and the spatial-temporal correlations within airport networks. Rigorously evaluated on two extensive real-world datasets, our model consistently outperforms current state-of-the-art baseline models, showcasing superior predictive performance and the effective learning capabilities of its intricately designed modules. Our research highlights the criticality of analyzing spatial-temporal relationships and the dynamics of flight coupling, offering significant theoretical and practical contributions to the advancement and management of air transportation systems.
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Quant-GPT: Money is All You NeedYuzhe Yang , Kangqi Yu , and Juanquan Peng2024A Large Language Model for A-share Market InvestmentThis paper introduces Quant-GPT, a novel multi-agent optimized for Ashare market investment decisions. Leveraging a fine-tuning combination of distilled sentiment analysis from ChatGPT and real-world market data, the prediction agent of Quant-GPT addresses the challenges of model collapse and weak causality between sentiment and expected returns. Our methodology integrates a Retrieval-Augmented Generation (RAG) agent and summary agent, enabling the model to access relevant news articles and corporate announcements summary with concise investment information to enhance investment decision-making. The inclusion of diverse datasets and RAG significantly improves the model’s ability to forecast market trends and returns accurately. Experimental results demonstrate Quant-GPT’s superior performance over existing open-source LLMs in terms of annualized return, maximum draw-down, and Sharpe ratio. These findings underscore the potential of advanced language models in financial applications, providing a robust framework for integrating natural language understanding with quantitative investment strategies. The code is available on GitHub: https://github.com/TobyYang7/Quant-GPT
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Travel Insurance Recommendation AI System Based on Flight Delay Predictions and Customer SentimentYuzhe Yang , Haoqi Zhang , Zhidong Peng , and 2 more authors2024Predicting Purchase Intentions for Dynamic Insurance PricingIn this project, we designed an AI system to identify potential travel insurance intentions of customers. Our designed large language model (LLM), named Insurance-GPT, is capable of analyzing in real-time during interactions with users and it utilizes deep learning model to accurately predict flight delay. This provides a good user experience, as well as provides a reference for pricing strategies to insurance companies. The Insurance-GPT can be downloaded at https://modelscope.cn/models/TobyYang7/ InsuranceGPT. Additionally, the complete source code for this project is available on GitHub at https://github.com/TobyYang7/ Travel-Insurance-Recommendation-AI-System.
