{"repo":"WinstonLiyt/MGGAL","free":true,"listed":false,"github":"https://github.com/WinstonLiyt/MGGAL","clone":"git clone https://github.com/WinstonLiyt/MGGAL.git","description":"[TKDE] The official code implementation for Financial Time Series Prediction with Multi-granularity Graph Augmented Learning.","language":"Python","stars":12,"topics":["ai-for-finance","data-mining","quantitative-finance","time-series-forecasting"],"license":"MIT","category":"trading","readme_excerpt":"Financial Time Series Prediction with Multi-granularity Graph Augmented Learning [TKDE] The official code implementation for Financial Time Series Prediction with Multi-granularity Graph Augmented Learning (MGGAL). Overview Multi-Granularity Graph-Augmented Learning framework ( MGGAL ) is a multi-scale graph-based deep learning architecture tailored for financial time series forecasting . Unlike conventional sequence models that focus solely on temporal dependencies, MGGAL explicitly integrates multi-frequency temporal dynamics (weekly, daily, and intraday 5-minute levels) with graph neural networks (GNNs) to capture evolving inter-stock relationships. The framework introduces three major innovations: ➊ Multi-Granularity Temporal Representation: Independent GRU encoders extract temporal embeddings at weekly, daily, and minute-level resolutions, ensuring that both long-horizon market trends and short-term fluctuations are preserved. ➋ Temporal Return Relationship Graph: Stock correlations are computed using rolling-window Pearson coefficients at each granularity. Thresholding generates adjacency matrices that encode inter-stock dependencies across scales, allowing the model to reason over heterogeneous, time-varying correlations. ➌ Attentional Graph Augment Module: Return-based graphs are dynamically refined with fundamental features (e.g., P/E ratio, market capitalization, dividend yield) via MLP-based relation weighting. This produces context-aware graph structures adaptive ","default_branch":null,"files":null,"tree":[],"storefront":"/r/WinstonLiyt","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/WinstonLiyt/MGGAL/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}