← Models and prediction modes

Purpose and workload fit

Fits successive trees to residuals using lags, calendars, and covariates. Supports squared-error point forecasts or a native quantile objective.

History and runtime requirements

At least lags + 4×min_samples_leaf + 1 observations; direct mode adds horizon−1.

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.

Supports covariates on the same sample grid, using only observations before the forecast origin. Future covariates carry the last observed value; future business demand is not assumed known.

Parameter reference

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

Parameter / typeDefault / range / unitMeaning and use
lags
int		Required; no default
≥ 1; ≤ 2000; steps		Number of preceding target observations used as features.
calendar_features
bool		false
No additional range		Include UTC daily and weekly sine/cosine features.
n_estimators
int		100
≥ 1; ≤ 1000		Number of trees; more trees increase fitting cost.
learning_rate
float		0.1
≥ 0.001; ≤ 1		Shrinkage applied to each boosting tree.
max_depth
int		3
≥ 1; ≤ 8		Maximum tree depth; deeper trees can overfit.
min_samples_leaf
int		5
≥ 1		Minimum observations per leaf.
objective
string		"squared"
squared / quantile		squared is point forecasting; quantile uses the alpha quantile and needs coverage evaluation.
alpha
float		0.9
≥ 0.01; ≤ 0.99		Native quantile level, used only when objective=quantile.
training_window_steps
int		5000
≥ 0; steps		Use only this many recent observations for training; zero uses all available history.
covariate_lags
int		3
≥ 1; ≤ 48; steps		Lag count per covariate. Future steps carry forward the last observed covariate; ignored without covariates.
multi_step
string		"recursive"
recursive / direct		recursive feeds forecasts forward; direct fits separate horizon anchors without recursive feedback.
direct_anchors
int		8
≥ 2; ≤ 60		Number of directly trained horizon anchors, with interpolation between them; used only in direct mode.

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
{
  "lags": 24,
  "n_estimators": 100,
  "learning_rate": 0.1,
  "max_depth": 3,
  "training_window_steps": 5000,
  "multi_step": "recursive",
  "objective": "squared"
}

Tuning and tradeoffs

Smaller learning_rate generally needs more trees. alpha denotes a quantile only when objective=quantile, unlike EWMA alpha. Compare native quantiles and conformal calibration separately before stacking upward adjustments.

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