Practical Time Series Modelling and Forecasting in R

Sequential data is everywhere, from fluctuating stock prices and energy demand to website traffic and sales trends. Understanding how to analyze and predict these patterns is a vital skill for data professionals. This course offers a clear, structured pathway to mastering time series analysis using the R programming language, focusing on practical application and modern coding practices. You will transition from understanding basic sequential patterns to building robust, production-ready forecasting models. Through written explanations and clear code snippets, you will learn how to handle real-world temporal data, diagnose underlying patterns, and generate reliable future predictions. What you'll learn: - Understand foundational time series concepts, including trend, seasonality, stationarity, and autocorrelation. - Clean and structure temporal datasets using modern, tidyverse-aligned time series packages. - Build classic statistical models including ARIMA, SARIMA, and Exponential Smoothing (ETS). - Evaluate forecasting accuracy using key metrics like MAE, RMSE, and modern cross-validation techniques. - Apply your modeling skills to real-world business, finance, and economic scenarios. This course begins with key terminology and data structures before guiding you through exploratory analysis, model selection, and validation. It is designed for beginners, data analysts, and aspiring data scientists who want to build forecasting skills without needing advanced mathematical prerequisites. Start reading today to unlock the power of predictive data analysis with R.