Common Patterns in Time Series: Seasonality, Trend and Autocorrelation

Course link: https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction Time-series come in all shapes and sizes, but there are a number of very common patterns. So it's useful to recognize them when you see them. For the next few minutes we'll take a look at some examples. The first is trend, where time series have a specific direction that they're moving in. As you can see from the Moore's Law example we showed earlier, this is an upwards facing trend. Another concept is seasonality, which is seen when patterns repeat at predictable intervals. For example, take a look at this chart showing active users at a website for software developers. It follows a very distinct pattern of regular dips. Can you guess what they are? Well, what if I told you if it was up for five units and then down for two? Then you could tell that it very clearly dips on the weekends when less people are working and thus it shows seasonality. Other seasonal series could be shopping sites that peak on weekends or sport sites that peak at various times throughout the year, like the draft or opening day, the All-Star day playoffs and maybe the championship game. Of course, some time series can have a combination of both trend and seasonality as this chart shows.

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