Master the principles and practice of time series forecasting with R and build the skills to analyze historical data and generate reliable predictions. This course takes you through a structured learning journey, beginning with the fundamentals of forecasting in business analytics before progressing to regression, decomposition, and advanced forecasting models.

You will learn how to distinguish between qualitative and quantitative forecasting methods, apply simple forecasting techniques, evaluate forecast accuracy, and address common forecasting challenges. As you advance, you will use simple, multiple, and non-linear regression, incorporate predictor and lagged variables, and decompose time series into trend, seasonal, cyclical, and irregular components to create more interpretable forecasting models. The course concludes with advanced techniques, including exponential smoothing, ARIMA, and Seasonal ARIMA (SARIMA), while using ACF and PACF diagnostics to support effective model selection and implementation in R. Designed for learners who want to strengthen their forecasting capabilities, this course combines foundational concepts with practical implementation in R through a logical, step-by-step progression. By the end of the course, you will be able to select appropriate forecasting methods, build and evaluate time series forecasting models, and develop accurate forecasting solutions that support data-driven decision-making across a wide range of business applications.