Author Description

This is a collection of lecture notes on applied time series analysis and forecasting using the statistical programming language R. <p>Many of these lectures are based on the original notes by Y. R. Gel and C. Cutler for the course STAT-443 *Forecasting* (University of Waterloo, Canada) adapted and expanded by V. Lyubchich for the course MEES-713 *Environmental Statistics 2* (University of Maryland, USA). <p>Each lecture starts by listing the learning objectives and required reading materials, with additional references in the text.<br>The notes introduce the methods and give a few examples but are less detailed than the reading materials. The notes do not substitute a textbook. The audience is expected to be familiar with R programming and the following statistical concepts and methods: probability distributions, sampling inference and hypothesis testing, correlation analysis, and regression analysis (including simple and multiple linear regression, mixed-effects models, generalized linear models, and generalized additive models).