R Tutorial : White noise

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. --- Let me show you the most boring time series you will ever see. It is just random, independent and identically distributed observations. In short-hand, statisticians often call this "iid data". In other words, there is nothing going on -- no trend, no seasonality, no cyclicity, not even any autocorrelations. Just randomness. In time series, we call it "white noise". The name comes from physics where white light has some similar mathematical characteristics. Although it appears boring, it is a very important type of time series because it is the basis of almost all forecasting models. The autocorrelation() function of white noise consists of many insignificant spikes. Because the data is simply random, we expect correlations between observations to be close to zero. The dashed blue lines are there to show us how large a spike has to be before we can consider it significantly different from zero. In this example, the first 15 spikes are all within the blue lines, as we would expect. Even the largest spike at lag 10 is well within the range we would expect for a white noise series. The blue lines are based on the sampling distribution for autocorrelation assuming the data are white noise. Any spike within the blue lines should be ignored. Spikes outside the blue lines might indicate something interesting in the data. At least, they suggest there may be some information that we could use in building a forecasting model. Here is a time series showing the number of pigs slaughtered each month in my home state of Victoria. This is the series underpinning the pork chops and bacon I sometimes enjoy. At first glance, it looks relatively random. Possibly there's a slight upward trend, but it is hard to see the difference between this and a white noise series on