Quick Start

Get up and running with LOWESS in minutes.

Basic Smoothing

Smooth a noisy sine wave — the kind of signal where LOWESS shines. Each example recovers the underlying trend from 100 points of Gaussian noise.

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

// 100-point noisy sine wave
const n = 100;
const x = Float64Array.from({ length: n }, (_, i) => i * 2 * Math.PI / (n - 1));
const y = Float64Array.from(x, (xi, i) => Math.sin(xi) + (((i * 7 + 3) % 17) / 17 - 0.5) * 0.6);

const model = new Lowess({ fraction: 0.3, iterations: 3 });
const result = model.fit(x, y);

console.log(`First smoothed value: ${result.y[0].toFixed(4)}  (true: ${Math.sin(x[0]).toFixed(4)})`);

First smoothed value: 0.0273  (true: 0.0000)

---

With Confidence Intervals

const { Lowess } = 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 model = new Lowess({
    fraction: 0.5,
    iterations: 3,
    outputs: ["diagnostics"],
    intervals: { confidence: 0.95, prediction: 0.95 }
});
const result = model.fit(x, y);

console.log("Smoothed (first 5):", [...result.y.slice(0, 5)].map(v => v.toFixed(4)));
console.log("CI lower (first 5):", [...result.confidence_lower.slice(0, 5)].map(v => v.toFixed(4)));
console.log("CI upper (first 5):", [...result.confidence_upper.slice(0, 5)].map(v => v.toFixed(4)));
console.log("R2:", result.diagnostics.r_squared.toFixed(4));

Smoothed (first 5): [ '0.3325', '0.3593', '0.3871', '0.4160', '0.4458' ]
CI lower (first 5): [ '0.2929', '0.3190', '0.3462', '0.3743', '0.4034' ]
CI upper (first 5): [ '0.3722', '0.3995', '0.4280', '0.4576', '0.4881' ]
R2: 0.9668

---

Handling Outliers

LOWESS can robustly handle outliers through iterative reweighting:

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

const xOut = new Float64Array([1, 2, 3, 4, 5, 6]);
const yWithOutlier = new Float64Array([2.0, 4.0, 6.0, 50.0, 10.0, 12.0]);

const model = new Lowess({
    fraction: 0.7,
    iterations: 5,
    robustness_method: "bisquare",
    outputs: ["weights"]
});
const result = model.fit(xOut, yWithOutlier);

// Outliers will have low robustness weights
result.robustness_weights.forEach((w, i) => {
    if (w < 0.5) {
        console.log(`Point ${i} is likely an outlier (weight: ${w.toFixed(3)})`);
    }
});

Point 3 is likely an outlier (weight: 0.000)

---

Streaming Mode

For datasets too large to fit in memory, stream them in fixed-size chunks with overlap.

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

const n = 5000;
const x = Float64Array.from({ length: n }, (_, i) => i * 10 * Math.PI / (n - 1));
const y = Float64Array.from(x, (xi, i) =>
    Math.sin(xi / Math.PI) * Math.exp(-xi / 30) +
    (((i * 7 + 3) % 17) / 17 - 0.5) * 0.3
);

const model = new StreamingLowess(
    { fraction: 0.2 },
    { chunk_size: 1000, overlap: 100, merge_strategy: 'weighted_average' }
);

const chunk_size = 1000;
for (let start = 0; start <= 4000; start += chunk_size) {
    const end = Math.min(start + chunk_size, n);
    model.process_chunk(x.slice(start, end), y.slice(start, end));
}
const result = model.finalize();
console.log(`Smoothed ${result.y.length} points in streaming mode`);

Smoothed 100 points in streaming mode

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Next Steps

Topic	Link
How LOWESS works	Concepts
All parameters explained	API Reference
Batch vs Streaming vs Online	Execution Modes
Edge handling	Boundary
Outlier handling in depth	Robustness
Full API per language	API Reference