![]() Sum returns the sum of all values in the collection.Min and Max return the smallest and largest value in the collection.Average returns the sum of all values divided by the total number of values.There are several ways to aggregate time series data. To compare the temperature in August over the years, you’d have to combine the 31 times 24 data points into one.Ĭombining a collection of measurements is called aggregation. What if you wanted to compare periods longer than the interval between measurements? If you’d measure the temperature once every hour, you’d end up with 24 data points per day. ![]() Aggregating time seriesĭepending on what you’re measuring, the data can vary greatly. If you know that the system load peaks every day around 18:00, you can add more machines right before. By identifying these periodic, or seasonal, time series, you can make confident predictions about the next period. For example, the temperature is typically higher during the day, before it dips down at night. 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. Create a free account to get started, which includes free forever access to 10k metrics, 50GB logs, 50GB traces, 500VUh k6 testing & more. You can use Grafana Cloud to avoid installing, maintaining, and scaling your own instance of Grafana.
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