Practical Time Series Analysis and Forecasting with R

Understanding how data changes over time is a critical skill for making informed, data-driven decisions in any industry. This course provides a clear pathway to analyzing historical patterns and forecasting future trends using the powerful R programming language. You will transition from understanding basic data concepts to confidently building, evaluating, and visualizing time series models. Through written explanations and practical code walkthroughs, you will learn how to handle real-world challenges like missing data, seasonal fluctuations, and trend analysis using modern R packages. What you'll learn: - Understand foundational time series concepts, terminology, and statistical definitions - Clean and preprocess temporal data, including handling missing values and anomalies - Visualize data trends and seasonal patterns using ggplot2 and modern plotting workflows - Apply exponential smoothing and ARIMA models to generate reliable future forecasts - Evaluate model performance using time series cross-validation techniques - Analyze real-world datasets, including employment rates, inflation, and financial market data The course begins with essential definitions and core time series concepts before guiding you through step-by-step practical implementations. You will work through structured scenarios that build your skills progressively, from basic visualizations to advanced forecasting models. This course is designed for beginners, aspiring data analysts, and professionals looking to add forecasting to their skill set. No prior experience with advanced statistics or time series modeling is required, though a basic familiarity with R syntax is helpful. Start reading today and unlock the power of predictive data analysis with R.