R Tutorial : Welcome to Forecasting Using R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/forecasting-using-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi. I'm Rob Hyndman. I'm a Professor of Statistics at Monash University in Australia. I'll be your instructor for this DataCamp course on Forecasting In R. In this course, we will learn how to visualize time series in order to discover the useful information we need for forecasting. We will also consider some very simple forecasting methods, some intermediate level methods such as exponential smoothing and ARIMA models, as well as some more advanced methods. Throughout the course, we will measure how accurate our forecasts are, and how to decide which method to use in each case. Everything we cover in this course is discussed in more detail in my textbook with George Athanasopoulos. It is freely available online, so you can always refer to it if you want more information. The book uses R throughout and shows the code for almost all graphs and analyses. Prediction is a big topic, and in this course, we are going to focus on a particular type of prediction, namely forecasting time series. A time series is simply a series of data observed over time. In this course, we deal only with regularly spaced time series. For example, the data could be observed every hour, every day, every month, every quarter, or every year. Provided the observation intervals are equally spaced, we call them a regularly spaced time series. Here is an example of a monthly time series of total expenditure on eating out in Australia. There is a strong trend, driven by a mix of population growth and an increase in disposable income, and there is some seasonality. In recent years, eating-out costs have peaked in December (due to Christmas and end-of-year events) and drop in February (due to it being a short month). Forecasting is the task of estimating how a time series like this will