Use Forecasting in R for Data-Driven Decision Making
This course provides an introduction to time series forecasting using R.

Forecasting involves making predictions about the future. It is required in many situations, such as deciding whether to build another power generation plant in the next ten years or scheduling staff in a call center next week.

Forecasts may be needed several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, reliable forecasting is essential to good data-driven decision-making.

Build Accurate Forecast Models with ARIMA and Exponential Smoothing
You’ll start this course by creating time series objects in R to plot your data and discover trends, seasonality, and repeated cycles. You’ll be introduced to the concept of white noise and look at how you can conduct a Ljung-Box test to confirm randomness before moving on to the next chapter, which details benchmarking methods and forecast accuracy.

Being able to test and measure your forecast accuracy is essential for developing usable models. This course reviews a variety of methods before diving into exponential smoothing and ARIMA models, which are two of the most widely-used approaches to time series forecasting.

Before you complete the course, you’ll learn how to use advanced ARIMA models to include additional information in them, such as holidays and competitor activity.