← Models and prediction modes

Purpose and workload fit

Smooths recent observations and projects the resulting level. A low-cost candidate for recent demand, without explicit daily seasonality.

History and runtime requirements

At least two observations.

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

On narrow screens, swipe the parameter table horizontally to read all columns.

Parameter / typeDefault / range / unitMeaning and use
alpha
float		0.3
≥ 0.001; ≤ 1		Smoothing coefficient; larger values react faster. Use either explicit alpha or a positive span.
span
int		0
≥ 0; ≤ 100000; steps		Alternative EWMA span; a positive value derives alpha=2/(span+1). Do not also set alpha explicitly.
bias_correction
bool		false
No additional range		Correct the EWMA initialization bias.

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.

JSON
{
  "alpha": 0.3,
  "bias_correction": false
}

Tuning and tradeoffs

A larger alpha reacts faster but follows more noise. span is an alternative way to specify smoothing; do not supply explicit alpha together with a positive span.

Shared parameters: shifts and quantiles

Parameter / typeDefault / range / unitMeaning and use
reset_on_change
bool		false
No additional range		Use history after a detected level shift, preserving enough points for the underlying method.
change_threshold
float		8
≥ 3; ≤ 50		Minimum shift relative to robust noise; used only with reset_on_change.
change_min_shift
float		0.2
≥ 0; ≤ 5		Minimum relative level shift; 0.2 means 20%. Used only with reset_on_change.
quantile_level
float		0
≥ 0; ≤ 0.99		Zero disables conformal calibration; otherwise must be above 0.5 and at most 0.99. Calibrates an upper forecast using earlier forecast errors.
calibration_windows
int		8
≥ 3; ≤ 50		Historical windows for conformal calibration; more windows add forecast calls and history requirements.

quantile_level uses a 0–1 scale; percentile uses 1–100. Calibration runs extra historical forecasts. Check history and runtime budgets before increasing windows; coverage is an evaluation target, not a guarantee.

Use in a module

  1. Save parameters and check history, sample interval, and horizon.
  2. Enable module prediction. Fixed mode runs the selected method; dynamic mode uses centrally enabled methods.
  3. Run rolling backtests and earn version promotion before adoption. Re-run backtests after changes affecting model identity.
  4. 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.

Backtesting and evaluation · Recommendations and look-ahead