Options

Options are a single JSON object in the last argument. Unknown keys and wrong-typed values are rejected with PREDICT_ERR_OPTIONS, so a typo fails loud rather than being ignored.

forecast

Key	Type	Default	Meaning
model	string	auto	A model id to pin, or auto (the default: best of the stats plus eligible registered students, per series).
confidence_level	number (0,1)	0.9	Nominal coverage of the interval.
interval_method	residual | conformal	residual	Interval construction.
folds	integer	20	Rolling origins used by auto / conformal.
gap	integer	0	Leakage guard between train and target.
candidates	string[]	stat models + eligible registered students	Narrows the auto pool.
context_limit	integer	4096	Cap on points fed to the model (the most recent are kept; a capped series reports status truncated).

Aggregate rules: the options argument and horizon must be the same value on every row of a group, and a SELECT string passed as the first argument is rejected with an error explaining that forecast is an aggregate over your rows. There are no column-naming keys: the argument positions carry the columns and GROUP BY carries the series split.

detect_anomalies

model and context_limit as above, except that model defaults to theta-classic (the residual detector) and context_limit defaults to 0: the detector scores the whole series unless you cap it. Plus:

Key	Type	Default	Meaning
anomaly_prob_threshold	number (0,1)	0.99	Probability above which a point is flagged.

The same aggregate rules apply.

backtest

An aggregate over your rows, like forecast. confidence_level, interval_method, folds, gap, and context_limit as for forecast. model differs: it must be a statistical model id or auto, and it defaults to theta-classic, not auto; a distilled student competes inside auto but cannot be pinned here. There are no column-naming keys: the argument positions carry the columns and GROUP BY carries the series split.

fit

fit([name,] f1, ..., fN, label [, options]) trains a native tabular student over your rows; the label is the last argument before the optional options, so there is no target option. An optional leading TEXT argument names and registers the model, fit('id', f..., label), mirroring predict('id', f...). The leading name and the options object must be constant within an aggregate group.

Key	Type	Default	Meaning
kind	gbt | tree	gbt	Student architecture.
task	classify | regress	classify	Default classify (the label is read as a class). Pass regress for a numeric target; a real-valued label is not inferred.
register	string	none	Register the model under this id and return the id; without it, fit returns a model blob. The id can instead be given as a leading positional argument, fit('id', ...); supplying it both ways raises PREDICT_ERR_OPTIONS.

predict

A scalar: predict(model, f1, ..., fN [, options]). model is a registered id or a fit() blob; features are positional.

Key	Type	Default	Meaning
proba	0 | 1	0	Return a {"prediction": "1", "confidence": 0.98} JSON document instead of the bare label.

predict_batch

The batched and in-context serving path, predict_batch(train_query, apply_query [, options]).

Key	Type	Default	Meaning
target	string	required for in-context models	Column in train_query to learn. A served model (train_query = NULL) needs no target.
task	classify | regress	inferred	Prediction task.
model	string	knn5-incontext	An in-context model, a registered native student id (trained by distill_predict or fit), or a registered onnx id. To serve an already-trained model (a native student or onnx), pass train_query = NULL; only in-context models take a train_query.
device	cpu | gpu	cpu	Inference device (onnx build).
precision	string	model default	Inference precision (onnx build).
accept_license	0 | 1	0	Accept a license-tagged model.

distill_predict

target and student_id are required. Feature columns must be numeric: encode categorical text before distilling (the in-context knn5-incontext handles text features itself; the distiller does not).

Key	Type	Meaning
target	string	Target column. Required.
task	classify | regress	Task. Inferred when omitted.
student_id	string	Id to register the student under. Required.
student_kind	tree | gbt | mlp	Student architecture. Default tree (soft-label distillation implies gbt).
teacher	string	A registered model that relabels the training rows first.
proba, classes	string[]	Soft-label distillation: per-class probability columns and their class labels.

distill_forecast

context, horizon, and student_id are required. Without a teacher, each train_query row is one training window laid out by position: context input columns, then the horizon continuation columns (or horizon * nquant quantile columns when quantiles is passed); the column count must match exactly.

Key	Type	Meaning
student_id	string	Id to register the student under. Required.
teacher	string	Teacher forecast model (onnx build): train_query then yields (series_key, value) rows and the teacher labels sliding windows.
context	integer	Input window length. Required; max 4096.
horizon	integer	Forecast length the student is trained for. Required.
quantiles	number[]	Quantile levels to distill from a teacher’s quantile fan. Default: a point (median-only) student.
hidden	integer	Residual-net hidden width, 0 to 2048 (0 = pure linear student). Default 256.
epochs, lr		Training epochs (default 1500) and learning rate (default 0.005).