This course offers an introduction to time series forecasting using R, a powerful tool for data-driven decision making. Forecasting plays a crucial role in various scenarios, from long-term capital investments to short-term scheduling. Accurate predictions are essential for making informed decisions based on data. In this course, you will learn how to create time series objects in R to analyze trends, seasonality, and repeated cycles. You will also explore the concept of white noise and conduct a Ljung-Box test to ensure randomness. To develop reliable forecast models, it is important to test and measure accuracy. This course covers various benchmarking methods and techniques for evaluating forecast accuracy. Furthermore, you will delve into exponential smoothing and ARIMA models, which are widely used in time series forecasting. These models will enable you to make accurate predictions based on historical data. Finally, you will discover how to enhance ARIMA models by incorporating additional information, such as holidays and competitor activity. By the end of this course, you will have the skills and knowledge to effectively use forecasting in R for data-driven decision making.