Forecasting is an algorithmic feature that allows you to predict where a metric is heading in the future. It is well-suited for metrics with strong trends or recurring patterns. For example, if your application starts logging at a faster rate, forecasts can alert you a week before a disk fills up, giving you adequate time to update your log rotation policy. Or, you can forecast business metrics, such as user sign-ups, to track progress against your quarterly targets.
To create a forecast monitor in Datadog, use the main navigation: Monitors –> New Monitor –> Forecast.
Any metric currently reporting to Datadog is available for monitors. For more information, see the Metric Monitor page.
After defining the metric, the forecast monitor provides two preview graphs in the editor:
1 month, etc.
Datadog automatically analyzes your chosen metric and sets several parameters for you. However, the options are available to edit under Advanced Options:
|Algorithm||The forecast algorithm (|
|Model||The forecast model (|
|Seasonality||The forecast seasonality (|
|Daylight savings||Available for |
|Rollup||The rollup interval—larger intervals between points avoid noise influence on the forecast.|
|Deviations||The width of the range of forecasted values—a value of 1 or 2 is generally large enough for most “normal” points.|
The available forecast algorithms are
Use the linear algorithm for metrics that have steady trends but no repeating seasonal pattern. There are three different models which control the linear algorithm’s sensitivity to level shifts:
|Default||Adjusts to the most recent trend and extrapolates data while being robust to recent noise.|
|Simple||Does a robust linear regression through the entire history.|
|Reactive||Extrapolates recent behavior better at the risk of overfitting to noise, spikes, or dips.|
Use the seasonal algorithm for metrics with repeating patterns. There are three different seasonality choices:
|Hourly||The algorithm expects the same minute after the hour behaves like past minutes after the hour, for example 5:15 behaves like 4:15, 3:15, etc.|
|Daily||The algorithm expects the same time today behaves like past days, for example 5pm today behaves like 5pm yesterday.|
|Weekly||The algorithm expects that a given day of the week behaves like past days of the week, for example this Tuesday behaves like past Tuesdays.|
Note: This algorithm requires at least two seasons of history and uses up to six seasons for forecasting.
For detailed instructions on the Say what’s happening and Notify your team sections, see the Notifications page.
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