In an era of rapid adaptations, on-again-off-again economic interventions, and emergent pandemics, data scientists use Time Series Analysis (TSA) to unlock insights about how things got this way and to predict where they will go next. Course coverage spans conceptual theoretical tools like Linear Systems and Fourier Analysis, to elementary frameworks like autoregressive modeling and advanced tools like State Space Models and Deep Learning forecasters. We aim to provide a firm understanding while still remaining friendly and engaging. Through hands-on Python/R projects you'll build, tune, and evaluate models. We'll explore applications in data-driven fashion and gain skills to shape the future of data analysis in science, engineering, business, healthcare, and beyond. This course will cover: Mathematical Foundations: Linear Systems Analysis, Linear Control Theory, Fourier Transforms, z-transforms, Spectral analysis, autocorrelation, autoregression. Statistical Generative Models. Stochastic Processes, Random Walks, Autoregressive models, ARIMA/SARIMA, State Space Models.Deep Learning Approaches: RNNs, LSTMs, UNet and Transformer-style architectures' Benchmarks and Datasets; Challenges and Evaluations; State-of-the-Art systems.
3 units · Letter or Credit/No Credit
In an era of rapid adaptations, on-again-off-again economic interventions, and emergent pandemics, data scientists use Time Series Analysis (TSA) to unlock insights about how things got this way and to predict where they will go next. Course coverage spans conceptual theoretical tools like Linear Systems and Fourier Analysis, to elementary frameworks like autoregressive modeling and advanced tools like State Space Models and Deep Learning forecasters. We aim to provide a firm understanding while still remaining friendly and engaging. Through hands-on Python/R projects you'll build, tune, and evaluate models. We'll explore applications in data-driven fashion and gain skills to shape the future of data analysis in science, engineering, business, healthcare, and beyond. This course will cover: Mathematical Foundations: Linear Systems Analysis, Linear Control Theory, Fourier Transforms, z-transforms, Spectral analysis, autocorrelation, autoregression. Statistical Generative Models. Stochastic Processes, Random Walks, Autoregressive models, ARIMA/SARIMA, State Space Models.Deep Learning Approaches: RNNs, LSTMs, UNet and Transformer-style architectures' Benchmarks and Datasets; Challenges and Evaluations; State-of-the-Art systems.
Offered in Spring 2026 at Stanford University.