Practical Time Series Analysis
State University of New York
Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
More resources on Time Series & Forecasting
otexts.com/fpp3
An online textbook that teaches time series forecasting with practical, code-driven examples in R. It covers forecasting methods (ETS, ARIMA, regression with time series), model evaluation, and case studies with hands-on tutorials using the fpp3 R package.
Time Series Analysis (MIT 14.384)
Time series methods in econometrics: stationary and non-stationary models, vector autoregressions, frequency-domain methods, persistent series, structural breaks, and estimating DSGE models by simulated moments, maximum likelihood and Bayesian methods. Provides 23 lecture note files and a problem set. Prepares you for empirical macroeconomic research.
Prophet Documentation
Official documentation for Prophet, Meta's open-source additive forecasting model for business time series in Python and R. Covers trend changepoints, seasonality, holiday effects, uncertainty intervals and diagnostics, with runnable examples for fitting and tuning models.
Time Series Analysis
Hamilton's graduate econometrics reference covering ARMA and spectral analysis, asymptotic theory, maximum likelihood, unit roots, cointegration, vector autoregressions, GMM, Kalman filtering and regime-switching models. Assumes matrix algebra and mathematical statistics; used as a doctoral text.
forecastingprinciples.com
Forecasting Principles is an online resource that teaches time series forecasting through clear explanations and practical examples. It covers key methods (exponential smoothing, ARIMA, seasonal models) and provides runnable code and case studies to apply forecasting techniques.