Forecast model configuration

This page contains details on the forecast model details available for configuration and relevant notes regarding the individual settings.

Standard Model Options

Uncertainty Interval Width

Default: 0.95
Minimum: 0.01
Maximum: 1

Uncertainty intervals (yhat_upper, yhat_lower) are computed as quantiles of the predicted value to use. The default value of 0.95 provides a 95% prediction interval, meaning 95% of future data should be expected to be between (yhat_upper, yhat_lower).

Upper and Lower Limits

Optionally you can set upper and lower limits for the forecasted values. When specifying these, be sure to choose values which correctly bound the underlying data. For example, if you are forecasting rates, you may want to set a lower limit of zero to ensure that the model does not predict negative values.

You must always define either neither or both of the limits.

Advanced Model Options

Trend Mode

Default: Auto
Options: Auto, Custom, Off

When set to Auto the series is assumed to have some trend component. Trend changepoints will be automatically detected in the first 80% of the data and are given a relatively weak weight in the model.

Set this option to Custom to expose two further options:

Changepoint range determines the proportion of data in which changepoints should be detected. For example, setting this to 0.5 would detect changepoints in the first 50% of the data.
Changepoint prior determines the weight given to changepoints in the model. Increasing this will allow changepoints to have a larger effect on the forecasts. Set this to a very low number to effectively disable changepoint detection.

Set this option to Off to disable the trend component entirely.

Daily Seasonality

Default: Auto
Options: Auto, Off, Custom

By default, daily seasonality is enabled if the query has at least two days of data.

You can set this option to Custom to customise the amount of detail modeled by the daily seasonality. This may be useful if your data shows strong daily patterns.

Set this option to Off to disable daily seasonality entirely.

Weekly Seasonality

Default: Auto
Options: Auto, Off, Custom

By default, weekly seasonality is enabled if the query has at least two weeks of data.

You can also set this option to Custom to customise the amount of detail modeled by the weekly seasonality. This may be useful if your data shows strong weekly patterns.

Set this option to Off to disable weekly seasonality entirely.

Seasonality Prior Scale

Default: 10
Minimum: 0.01
Maximum: 10

Controls the weight given to daily and weekly seasonalities in the models forecasts. Increasing this value will cause the model to follow seasonal trends more strongly.

Seasonality Mode

Default: Additive
Options: Additive, Multiplicative

How seasonality is applied to the trend to form the final forecast. Set to Multiplicative if it appears that the magnitude of seasonal fluctuations grows with the magnitude of the time series.

Data Range

Default: 90 days
Maximum: 5 years

How far back in time to look for training data. This range should not be greater than the retention period of the underlying data source, as no data would be returned beyond it.

The resolution of the training data and of the generated predictions (also known as the interval or step) will be determined based on this range, such that no more than 50,000 samples per series are included in the training data. This is done by finding the smallest interval from a few options such that data range / interval < 50000. For reference, 90 days of training data uses a 5 minute interval for a total of 25,920 samples. Larger ranges will therefore result in larger intervals.