Enroll Course: https://www.coursera.org/learn/bayesian-statistics-time-series-analysis
For anyone looking to elevate their data science and statistical modeling skills, the Coursera course ‘Bayesian Statistics: Time Series Analysis’ is an absolute gem. As the fourth installment in a comprehensive Bayesian statistics sequence, this course assumes a solid foundation in probability and builds directly upon prior knowledge from “Bayesian Statistics: From Concept to Data Analysis, Techniques and Models” and “Mixture models.” If you’re ready to tackle the complexities of temporally related variables, this course is your next essential step.
Time series analysis, at its core, is about understanding the dependencies within sequential data. This course expertly navigates this domain, starting with the fundamentals in Week 1. You’ll be introduced to stationary processes, the crucial autocorrelation function, and the autoregressive process of order one (AR(1)). The module doesn’t just stop at theory; it dives into practical parameter estimation using both maximum likelihood and Bayesian inference for the AR(1) model.
Building on this strong foundation, Week 2 expands your toolkit to the more general AR(p) process, again covering both maximum likelihood and Bayesian posterior inference. This systematic progression ensures a deep understanding of autoregressive models.
The real power of the course, however, unfolds in Weeks 3 and 4 with the introduction to Normal Dynamic Linear Models (NDLMs). These models are incredibly versatile for time series. You’ll learn how to define and illustrate NDLMs with practical examples, and crucially, how to build models using the forecast function and the superposition principle. The course also meticulously covers Bayesian filtering, smoothing, and forecasting for NDLMs, assuming known observational variances and system covariance matrices.
To solidify your learning, Week 5 offers a hands-on final project. Here, you’ll apply the powerful Normal Dynamic Linear Models to analyze a real-world time series dataset downloaded from Google Trends. This project is the perfect opportunity to synthesize everything you’ve learned and gain practical experience in applying Bayesian time series techniques.
Overall, ‘Bayesian Statistics: Time Series Analysis’ is an exceptionally well-structured and informative course. It strikes an excellent balance between theoretical rigor and practical application, making it highly recommendable for both practicing data scientists and aspiring statisticians eager to master time series analysis through a Bayesian lens.
Enroll Course: https://www.coursera.org/learn/bayesian-statistics-time-series-analysis