1. Welcome to Forecasting Using R
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.
2. What you will learn
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
3. Course textbook
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.
4. Time series data
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 continue into the future. Of course there is considerable uncertainty associated with such estimates, so we normally also provide an estimate of that uncertainty in the form of prediction intervals.
5. Forecasts of monthly Australian expenditure on eating out
This is an example of what such forecasts might look like. In this example, the forecast values are shown in blue, along with 80% prediction intervals and 95% prediction intervals. These indicate how uncertain our forecasts are. The further ahead we forecast, the wider these prediction intervals tend to be.
In this course we will discuss various forecasting methods for time series data that take account of trend, seasonality and other features of the data. We will also look at how we can measure the accuracy of these forecasts, and how to go about selecting a good forecasting model.
6. Let's practice!
That's enough background information. Let's get going with your first interactive exercise.