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Time Series Analysis with MIT
Learn time series modeling, prediction, and reinforcement learning in this comprehensive graduate-level course from MIT's Statistics and Data Science program.
Learn time series modeling, prediction, and reinforcement learning in this comprehensive graduate-level course from MIT's Statistics and Data Science program.
This graduate-level course from MIT delivers a comprehensive exploration of time series analysis through three key areas: structured model learning, prediction methods, and reinforcement learning for optimal intervention. Students will master techniques for analyzing time-stamped data sets, from financial markets to epidemiological trends. The course combines theoretical foundations with practical applications through hands-on projects using real data. As part of MIT's MicroMasters in Statistics and Data Science, it offers rigorous training in spectral analysis, ARMA modeling, matrix completion methods, and dynamic programming for time series optimization.
Instructors:
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What you'll learn
Analyze time series using Linear Time-invariant systems and spectral analysis methods
Develop and implement autoregressive moving average models for time series data
Apply matrix completion methods for prediction and data imputation
Optimize control and interventions using dynamical programming and reinforcement learning
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This comprehensive course covers time series analysis through three main modules. First, students learn structured models for understanding stochastic dynamic systems. Next, they explore prediction techniques using Matrix and Tensor Completion Methods. Finally, they study optimal intervention and reinforcement learning, applying these concepts through hands-on projects with real data. The course emphasizes both theoretical understanding and practical implementation, preparing students for advanced data science applications.
Fee Structure
Instructors
25 Courses
MIT's Digital Learning Pioneer and Mathematics Education Innovator
Karene Chu serves as the Assistant Director of Education and Research Scientist at MIT's Institute for Data, Systems, and Society, where she has made significant contributions to digital learning initiatives. After receiving her Ph.D. in mathematics from the University of Toronto in 2012, she completed postdoctoral fellowships at both the University of Toronto/Fields Institute and MIT, specializing in knot theory and quantum invariants. In 2015, she transitioned to become a digital learning lab fellow at MIT, where she has since played a pivotal role in developing and managing the MicroMasters Program in Statistics and Data Science. Her educational impact includes co-authoring the MITx Calculus Series, which became a Top 10 edX course in 2016, and leading the development of a five-course series on differential equations. She is also a key instructor in MIT's Machine Learning with Python course alongside Regina Barzilay and Tommi Jaakkola. Her teaching excellence was first recognized at the University of Toronto, where she received a teaching award for her work in single and multi-variable calculus and linear algebra. As part of MIT's edX group, she collaborated with colleagues to earn the inaugural MITx Prize for Teaching and Learning in MOOCs, demonstrating her commitment to advancing digital education and making complex mathematical concepts accessible to learners worldwide.
11 Courses
A Pioneer in Control Theory and Network Systems
Munther A. Dahleh, the William A. Coolidge Professor in Electrical Engineering and Computer Science at MIT, has established himself as a leading figure in control theory and networked systems since joining MIT in 1987. After receiving his PhD from Rice University in electrical and computer engineering, he has made transformative contributions to the field, serving as the founding director of the Institute for Data, Systems, and Society from 2015 to 2023. His groundbreaking research spans robust control theory, networked systems analysis, and the development of computational methods for controller design. Dahleh's work has earned him numerous accolades, including four George Axelby outstanding paper awards and the Donald P. Eckman award for best control engineer under 35. His research interests encompass networked systems theory, social network analysis, systemic risk detection, and transportation systems optimization. He has pioneered work in L1 optimal control problems and developed foundational theories for information propagation in large dynamic networks. Beyond his academic contributions, Dahleh has held consulting positions with various companies and has been instrumental in advancing the understanding of complex systems, from autonomous vehicles to power grids. His current work focuses on the intersection of physical and information networks, the economics of data, and the development of real-time markets for digital goods, making him a key figure in shaping the future of networked systems and control theory.
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