OnlineLowess API

See also: fastLowess

When to Use

• Data arrives incrementally (sensors, streams)
• Need real-time smoothed values
• Fixed memory budget

Class

OnlineLowess

The OnlineLowess class updates the model incrementally with new data points.

Constructor:

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

const online = new OnlineLowess({ fraction: 0.5 }, { window_capacity: 50, min_points: 3 });
// Feed enough points to pass min_points threshold
for (let i = 0; i < 4; i++) {
    const result = online.add_point(i, Math.sin(i * 0.5));
    if (result !== null) console.log("Online smoothed at x=" + i + ":", result.y.toFixed(4));
}

Online smoothed at x=2: 0.8415
Online smoothed at x=3: 0.9975

• options: An object containing OnlineSmoothOptions fields (a subset of the Batch LowessOptions fields — see below).
• onlineOptions: An object containing OnlineOptions fields.

add_point(x, y)

Adds a single point to the sliding window and returns the smoothed value for that point, or null while the window is still filling up (fewer than min_points seen so far). Once the window reaches window_capacity, each new point evicts the oldest one, so memory stays bounded regardless of how much history has passed through. update_mode controls how much work each call does: "incremental" re-fits only the newest point, while "full" re-smooths the entire window for a more accurate but slower result.

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

const n = 100;
const x = Float64Array.from({ length: n }, (_, i) => i * 2 * Math.PI / (n - 1));
const y = Float64Array.from(x, xi => Math.sin(xi) + 0.1);

const online = new OnlineLowess({ fraction: 0.5 }, { window_capacity: 50, min_points: 3 });

// Returns null until min_points (3) are reached
online.add_point(x[0], y[0]);  // null
online.add_point(x[1], y[1]);  // null

// Returns OnlineOutput once enough points are available
const result = online.add_point(x[2], y[2]);
console.log("Smoothed y:", result.y);

Smoothed y: 0.22659245357374927

Options Structure

OnlineSmoothOptions

Field	Type	Default	Description
fraction	number	0.67	Smoothing fraction (bandwidth)
iterations	number	0	Number of robustifying iterations (requires update_mode = "full")
weight_function	string	"tricube"	Weight function name
robustness_method	string	"bisquare"	Robustness method name
delta	number	NaN	Interpolation distance (NaN disables interpolation); positive values require update_mode = "full"
zero_weight_fallback	string	"use_local_mean"	Zero-weight handling
boundary_policy	string	"extend"	Boundary handling policy
scaling_method	string	"mad"	Residual scaling method
auto_converge	number	null	Auto-convergence tolerance; requires update_mode = "full" and iterations > 0
missing	string	"error"	Policy for non-finite (NaN/Inf) values in each point
window_capacity	number	1000	Max points in sliding window
min_points	number	2	Min points before smoothing starts
update_mode	string	"incremental"	Update mode ("full" or "incremental")
outputs	string[]	[]	Select se, weights, and/or derivative; se requires update_mode: "full"
intervals	object	null	Grouped confidence, prediction, and per-window bootstrap options (requires update_mode: "full")
seed	number	null	Reproducible bootstrap draws for each full-update window; must be an integer from 0 through Number.MAX_SAFE_INTEGER

Incremental mode fits only the newest point. Positive delta is rejected there, and auto_converge requires full mode with at least one robustness iteration.

Cross-validation, GPU backend, custom_weights, the "sorted" output, and parallel are Batch-only; the "diagnostics" and "residuals" outputs are not available online. See fastLowess for those options.

Options

fraction

fraction is the most important parameter: it controls the size of the local neighbourhood used at each point.

Range	Effect	Use case
0.1-0.3	Fine detail	Rapidly changing signals
0.3-0.5	Balanced	General purpose
0.5-0.7	Heavy smoothing	Noisy data
0.7-1.0	Very smooth	Trend extraction

iterations

iterations controls robustness to outliers, at the cost of speed. Requires update_mode = "full"; the default "incremental" mode performs a non-robust single-point fit.

Value	Effect	Performance
0	No robustness	Fastest
1-3	Moderate	Recommended
4-6	Strong	Contaminated data
7+	Very strong	Heavy outliers

weight_function

See: Weight Functions

• "tricube" (default)
• "epanechnikov"
• "gaussian"
• "uniform" (alias: "boxcar")
• "biweight" (alias: "bisquare")
• "triangle" (alias: "triangular")
• "cosine"

robustness_method

See: Robustness

• "bisquare" (default; alias: "biweight")
• "huber"
• "talwar"

delta

Points within delta of each other on the x-axis share the same local fit instead of each computing its own regression — an interpolation shortcut that trades a small amount of accuracy for a large speedup on dense, evenly-spaced data. NaN (default) auto-sets it to 0 in Online mode, i.e. interpolation is disabled and every point is fit exactly.

zero_weight_fallback

Behavior when all neighborhood weights are zero:

Option	Behavior
"use_local_mean" (default; aliases: "local_mean", "mean")	Use the mean of the neighborhood
"return_original" (alias: "original")	Return the original y value
"return_none" (alias: "none")	Return NaN

boundary_policy

See: Boundary Handling

• "extend" (default; alias: "pad")
• "reflect" (alias: "mirror")
• "zero"
• "noboundary" (alias: "none")

scaling_method

See: Scaling Methods

• "mad" (default; alias: "median_absolute_deviation")
• "mar" (alias: "median_absolute_residual")
• "mean" (alias: "mean_absolute_residual")

auto_converge

See: Robustness

Convergence tolerance for early stopping of robustness iterations. null (default) disables early stopping.

missing

Policy for handling a non-finite (NaN/Inf) x or y value passed to add_point:

Option	Behavior
"error" (default)	Throw an error
"drop"	Silently ignore the point — add_point returns null instead of adding it to the window

window_capacity

Maximum number of most recent points kept in the sliding window; older points are discarded as new ones arrive. Each add_point() call costs O(window_capacity) rather than growing with total history.

min_points

Minimum number of points required before smoothing starts. add_point() returns null until the window reaches this size.

update_mode

See: Execution Modes

Mode	Alias	Behavior	Speed
"incremental" (default)	"single"	Update only affected fits	Faster
"full"	"resmooth"	Recompute entire window	More accurate

outputs: se

See: Intervals

Select "se" to populate standard_error; it requires update_mode: "full". The fast "incremental" path never computes standard errors, so selecting "se" (or setting intervals) with anything other than "full" throws at construction time.

outputs: weights

Select "weights" to include the robustness weight for the latest point (from the last robustness iteration) in the result.

outputs: derivative

Select "derivative" to expose the latest point’s local WLS slope in OnlineOutput.derivative at effectively no extra computation cost.

intervals

See: Intervals

An object such as { confidence: 0.90, prediction: 0.99, bootstrap: 200 }, populating confidence_lower/confidence_upper and prediction_lower/prediction_upper for the latest point. The coverage levels are independent. Same update_mode: "full" requirement as the "se" output. bootstrap (at least 2) refits each sliding window from resampled residuals.

seed

Seeds bootstrap draws. Each full-update window restarts from the same seed. 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.

Result Structure

OnlineOutput

Returned by add_point() once the window has enough points (null until then).

Field	Type	Description
y	number	Smoothed value for the latest point
standard_error	number | null	Populated when "se" or any interval is set (requires update_mode: "full"); otherwise always null
confidence_lower / confidence_upper	number | null	Confidence interval bounds around the mean response, if intervals.confidence was set (requires update_mode: "full")
prediction_lower / prediction_upper	number | null	Prediction interval bounds for a new observation, if intervals.prediction was set (requires update_mode: "full")
residual	number | null	Residual y − smoothed; always present (there is no "residuals" output for Online)
robustness_weight	number | null	Robustness weight, if "weights" was requested
iterations_used	number | null	Robustness iterations performed
derivative	number | null	Local fit derivative/slope for the latest point, if "derivative" was requested

There is no Diagnostics object or "diagnostics" output for OnlineLowess: OnlineOutput carries no diagnostics field, since diagnostics like RMSE/R² need more than one point’s worth of history to be meaningful.