Some time series have patterns that repeat themselves over a known period. If the number of registered users has been increasing monthly by 4% for the past few months, you can predict how big your user base is going to be at the end of the year. Time series can also help you predict the future, by uncovering trends in your data. Time series could tell you that the server crashed moments after the free disk space went down to zero. They help you understand the past by letting you analyze the state of the system at any point in time. Measurements are seldom updated after they were added - for example, yesterday’s temperature doesn’t change.New data is appended at the end, at regular intervals - for example, hourly at 09:00, 10:00, 11:00, and so on.While each of these examples are sequences of chronologically ordered measurements, they also share other attributes: Temperature data like the one in the example, is far from the only example of a time series. Visual representations like the graph make it easier to discover patterns and features of the data that otherwise would be difficult to see. A more common visualization for time series is the graph, which instead places each measurement along a time axis. Tables are useful when you want to identify individual measurements, but they make it difficult to see the big picture. Every row in the table represents one individual measurement at a specific time. Temperature data like this is one example of what we call a time series - a sequence of measurements, ordered in time. After a while, you’d have something like this: Time
Once every hour, you’d check the thermometer and write down the time along with the current temperature. Imagine you wanted to know how the temperature outside changes throughout the day. Intro to time series Introduction to time series