Practical Time Series Analysis and Forecasting in R

Organizations across every industry rely on historical data to predict future trends and make informed decisions. Mastering time series analysis allows you to unlock these temporal patterns and turn raw chronological data into actionable forecasts. In this course, you will transition from a beginner to a confident data analyst capable of preparing, visualizing, and modeling time-dependent data. You will master the foundational theory of temporal statistics and apply it directly using R, learning how to handle real-world complexities like time zones, missing values, and seasonality. What you'll learn: - Understand core time series concepts, including stationarity, autocorrelation, and seasonal decomposition. - Manage complex date and time formats in R using the lubridate package and modern data workflows. - Clean and prepare temporal data by handling missing values, identifying outliers, and transforming variables. - Build classic forecasting models such as ARIMA and exponential smoothing to predict future trends. - Apply modern tidy time series frameworks using packages like tsibble and fable for streamlined analysis. - Evaluate model performance using benchmark metrics to ensure highly accurate predictions. The course begins with foundational definitions of time series data and date-time manipulation in R before progressing to exploratory data analysis. You will then explore statistical testing, model building, and evaluation through detailed written explanations and practical coding exercises. This course is designed for beginners, data enthusiasts, and aspiring analysts who want to learn forecasting from scratch with no prior time series experience required. Start reading today to unlock the predictive power of your time-based data.