Intervals

Confidence and prediction intervals for uncertainty quantification.

Overview

Confidence and Prediction Intervals

Type	Represents	Width	Use
Confidence	Uncertainty in mean curve	Narrow	Where is the true trend?
Prediction	Uncertainty for new points	Wide	Where will new data fall?

Confidence Intervals

Estimate uncertainty in the smoothed curve itself.

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

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: 0.5, confidence_intervals: 0.95});
const result = model.fit(x, y);

result.y.slice(0, 3).forEach((y, i) => {
    console.log(`x=${result.x[i].toFixed(4)}: y=${y.toFixed(4)} [${result.confidence_lower[i].toFixed(4)}, ${result.confidence_upper[i].toFixed(4)}]`);
});
console.log(`... (${result.y.length - 3} more)`);

x=0.0000: y=0.1181 [0.0551, 0.1812]
x=0.0635: y=0.1502 [0.0762, 0.2243]
x=0.1269: y=0.1833 [0.1205, 0.2461]
... (97 more)

Prediction Intervals

Estimate where new observations might fall.

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

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: 0.5, prediction_intervals: 0.95});
const result = model.fit(x, y);
console.log(`Prediction bounds: [${result.prediction_lower[0]}, ${result.prediction_upper[0]}]`);

Prediction bounds: [-0.35046106311855035, 0.5866944919326049]

Both Intervals

Request both types simultaneously:

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

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: 0.5,
    confidence_intervals: 0.95,
    prediction_intervals: 0.95});
const result = model.fit(x, y);
console.log("CI lower[0]:", result.confidence_lower[0].toFixed(4));

CI lower[0]: 0.0551

Confidence Levels

Common levels and their z-values:

Level	z-value	Interpretation
0.90	1.645	90% of intervals contain true value
0.95	1.960	95% of intervals contain true value
0.99	2.576	99% of intervals contain true value

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

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);

// 99% confidence interval
const model = new Lowess({confidence_intervals: 0.99});
const result = model.fit(x, y);
console.log("CI lower[0]:", result.confidence_lower[0].toFixed(4));

CI lower[0]: 0.0789

Standard Errors

Access standard errors directly (available when intervals are computed):

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

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({confidence_intervals: 0.95});
const result = model.fit(x, y);

result.standard_errors.slice(0, 5).forEach((se, i) => {
    console.log(`Point ${i}: SE = ${se.toFixed(4)}`);
});
console.log(`... (${result.standard_errors.length - 5} more)`);

Point 0: SE = 0.0339
Point 1: SE = 0.0392
Point 2: SE = 0.0345
Point 3: SE = 0.0407
Point 4: SE = 0.0410
... (95 more)

Availability

Feature	Batch	Streaming	Online
Confidence intervals	✓	✗	✗
Prediction intervals	✓	✗	✗
Standard errors	✓	✗	✗