Modern time series data present complexities; these complexities, along with the rapidly growing array of new statistical and machine learning (ML) methods, have driven the demand for novel solutions to emerging problems. This CAREER project is driven by three fundamental research questions: (1) How to balance interpretability and accuracy in high-dimensional time series modeling and inference? (2) How to adaptively select time series models in real time for nonstationary data, while managing uncertainty? and (3) How to efficiently combine information from time series data with varying quality? This project aims to advance the field of time series analysis by developing novel statistical models, theories, and inference methods to address these issues. The results of this research will enhance dynamic network inference, facilitate real-time decision-making, and promote the integration of diverse time series data sources. This project will achieve educational impacts by integrating our research with mentoring undergraduate and graduate students, developing courses, and high-school outreach. Additionally, an interdisciplinary time series seminar series will be organized to promote cross-disciplinary interactions and provide students and junior researchers with exposure to diverse research in time series analysis. This project will advance time series analysis on three main fronts: (1) develop Granger causality interpretable, recurrent neural network-based high-dimensional tim