Purpose and workload fit
Repeats the latest observation across the horizon. Use it as a basic comparison and to check the data workflow; it cannot anticipate cycles or sudden changes.
History and runtime requirements
At least one valid observation.
Inputs must be regularly sampled, gap-free, and finite. Outputs retain the metric unit, such as CPU cores, memory bytes, or requests per second. The sample interval determines the duration of a step. Minimum history permits execution; it does not establish forecast quality.
This method does not use covariates; target metrics are forecast separately.
Parameter reference
No method-specific parameters; the parameter object is {}.
Configuration example
Edit parameters in Algorithm management. This is a parameter object, not a complete config.yaml. Seasonal examples assume five-minute sampling; adjust for your actual interval. Omitted optional values use the defaults above.
{}Tuning and tradeoffs
There are no algorithm parameters. Compare EWMA, Theta, or Seasonal Naive when trend or seasonality matters.
Use in a module
- Save parameters and check history, sample interval, and horizon.
- Enable module prediction. Fixed mode runs the selected method; dynamic mode uses centrally enabled methods.
- This baseline does not need backtest promotion, but actual performance still needs review.
- Review error, peak underestimation, runtime, and adoption reasons in Observe or Recommend before separately authorizing admission and replica actions.
For insufficient history, invalid settings, timeouts, or invalid output, inspect run reasons. Without eligible forecasts, the module follows its schedules, fallback, and protection rules.