This paper presents a framework using a temporal bipartite graph to model dependencies in time series. It uses autoregressive models for pattern identification and graph-based learning to capture transitions. The method aims to detect causal relationships in financial time series, comparing its accuracy and execution time with traditional methods like Granger causality and PCMCI. Designed for scalability and interpretability, its potential applications include risk assessment, portfolio management, and market behavior analysis in finance.

This paper introduces an unsupervised deep generative model that improves financial time series forecasting by learning latent representations and inter-series relationships using a dynamic attention mechanism. This approach significantly enhances the accuracy of multi-asset forecasts, making it valuable for financial applications like ETF and mutual fund trajectory prediction.