Out-of-Sample Prediction

Evaluate a fitted Batch model at query points that were not in the training set.

Overview

Out-of-sample prediction is available in Batch mode only. Streaming and Online modes do not support it.

result.predict(newX, options) evaluates the fit at arbitrary query points, like R’s predict(model, newdata).

It reuses fit()’s own (possibly delta-interpolated) smoothed curve for its y output — so predicting at a training x always exactly reproduces that point’s fit() output, regardless of delta. A fresh local fit is only run when outputs includes "derivative" or "se" (or an interval level), or when max_neighbor_distance needs the actual regression slope or standard error.

Requires retain_model: true on the constructor before fit(), otherwise predict() throws.

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Options

Field	Type	Default	Description
outputs	string[]	[]	Select "se" and/or "derivative"
intervals	object	null	Grouped confidence, prediction, and optional bootstrap options
seed	number	null	Reproducible prediction-time bootstrap draws; must be an integer from 0 through Number.MAX_SAFE_INTEGER
extrapolation	string	"clamp"	Behavior for query points outside the training x-range
max_extrapolation_distance	number	disabled	Under "linear" extrapolation, the max allowed distance beyond the training boundary before erroring
max_neighbor_distance	number	disabled	Max allowed distance to the farthest training point in a query’s local window before erroring

outputs

Request "se" for standard errors (from the retained model’s residual scale and per-point leverage) and "derivative" for the local slope at each query point. Analytic intervals compute their required standard errors even without an explicit "se" output.

intervals

An object such as { confidence: 0.95, prediction: 0.95, bootstrap: 200 }. confidence bounds the mean response and prediction bounds a new observation at each query point. Without bootstrap, these use normal-theory standard errors and the retained residual scale.

Set bootstrap to at least 2 to resample the retained Batch residuals, refit the model, and calculate query-point percentile intervals and standard errors.

seed

Seeds prediction-time bootstrap draws, independently of the fit/CV seed on Lowess. It does not enable bootstrap by itself. Seeds must be finite, non-negative safe integers no greater than Number.MAX_SAFE_INTEGER; fractional, negative, and unsafe values throw. 0 is valid.

extrapolation

Behavior for query points outside [min(x_train), max(x_train)]:

Policy	Behavior
"clamp" (default)	Clamps the query to the nearest boundary window
"linear"	Linearly extrapolates from the nearest boundary point’s local fit and slope
"error"	Fails the whole call with an error

max_extrapolation_distance

Under "linear" extrapolation, the maximum allowed distance beyond the training boundary before predict() throws, instead of returning an unbounded value. Disabled by default (uncapped).

max_neighbor_distance

Maximum allowed distance to the farthest training point in a query’s local window before predict() throws. Guards against an “empty range” blind spot: a query point can fall within [min(x_train), max(x_train)] yet still be far from any real training point (e.g. training x in [0,10] and [90,100], query at x=50). Disabled by default (uncapped); applies regardless of extrapolation.

Example

Basic Usage

const { Lowess } = require('fastlowess');

const x = new Float64Array([1, 2, 3, 4, 5]);
const y = new Float64Array([2.1, 4.0, 6.2, 8.0, 10.1]);

const model = new Lowess({ fraction: 0.7, retain_model: true });
const result = model.fit(x, y);

const prediction = result.predict(new Float64Array([1.5, 4.5]));
console.log("Predicted y:", prediction.y);

Predicted y: Float64Array(2) [ 3.05, 9.05 ]

Standard Errors and Derivative

const { Lowess } = require('fastlowess');

const x = new Float64Array([1, 2, 3, 4, 5]);
const y = new Float64Array([2.1, 4.0, 6.2, 8.0, 10.1]);

const model = new Lowess({ fraction: 0.7, retain_model: true });
const result = model.fit(x, y);

const prediction = result.predict(new Float64Array([2.5]), {
    outputs: ["se", "derivative"],
});
console.log(prediction.y, prediction.standard_errors, prediction.derivative);

Float64Array(1) [ 5.1 ] Float64Array(1) [ 0 ] Float64Array(1) [ 2.2 ]

Bootstrap Intervals

const { Lowess } = require('fastlowess');

const x = new Float64Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
const y = new Float64Array([2.1, 4.0, 6.2, 8.0, 10.1, 11.8, 14.2, 16.1, 17.9, 20.2]);

const model = new Lowess({ fraction: 0.7, retain_model: true });
const result = model.fit(x, y);

const prediction = result.predict(new Float64Array([2.5, 4.5]), {
    intervals: { confidence: 0.95, prediction: 0.95, bootstrap: 200 },
    seed: 7,
});
console.log("CI present:", prediction.confidence_lower !== null);

CI present: true

Linear Extrapolation

const { Lowess } = require('fastlowess');

const x = new Float64Array([1, 2, 3, 4, 5]);
const y = new Float64Array([2.1, 4.0, 6.2, 8.0, 10.1]);

const model = new Lowess({ fraction: 0.7, retain_model: true });
const result = model.fit(x, y);

const prediction = result.predict(new Float64Array([10.0]), {
    extrapolation: "linear",
});
console.log("Extrapolated y:", prediction.y);

Extrapolated y: Float64Array(1) [ 10.1 ]