Execution Modes

Choose the right adapter for your use case.

Overview

Choose the first row below whose condition applies:

Condition	Adapter
Data too large to fit in memory	Streaming
Fits in memory, need real-time/incremental updates	Online
Fits in memory, no real-time requirement	Batch

Mode	Use Case	Memory	Features
Batch	Complete datasets	Full	All features
Streaming	Large files (>100K)	Chunked	Residuals, robustness
Online	Real-time sensors	Fixed window	Incremental updates

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Batch Adapter

Standard mode for complete datasets. Supports all features.

When to Use

• Dataset fits in memory
• Need intervals, cross-validation, or diagnostics
• Processing complete files

Example

const { Loess } = require('fastloess-wasm');

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 Loess({
    fraction: 0.5,
    iterations: 3,
    parallel: true,
    outputs: ["diagnostics"],
    intervals: { confidence : 0.95, prediction : 0.95 }
});
const result = model.fit(x, y);
console.log("95% CI at midpoint: [" + result.confidence_lower[50] + ", " + result.confidence_upper[50] + "]");
console.log("R2:", result.diagnostics.r_squared);

95% CI at midpoint: [0.00041543067589761007, 0.1429101074299454]
R2: 0.9614463018021437

---

Streaming Adapter

Process large datasets in chunks with configurable overlap.

When to Use

• Dataset >100,000 points
• Memory-constrained environments
• Batch processing pipelines

Parameters

Parameter	Default	Description
chunk_size	5000	Points per chunk
overlap	500	Overlap between chunks
merge_strategy	"weighted_average"	How to merge overlaps

Merge Strategies

Strategy	Behavior
"average"	Average overlapping values
"weighted_average"	Distance-weighted blend (default)
"take_first"	Keep left chunk values
"take_last"	Keep right chunk values

Example

const { StreamingLoess } = require('fastloess-wasm');

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 stream = new StreamingLoess(
    { fraction: 0.3, iterations: 2 },
    { chunk_size: 5000, overlap: 500, merge_strategy: "average" }
);
stream.process_chunk(x, y);
const result = stream.finalize();
console.log("Smoothed y[0]:", result.y[0]);

Smoothed y[0]: 0.1308430266041239

---

Always call finalize()

The streaming adapter buffers overlap data. Call finalize() after processing all chunks to retrieve the buffered tail.

Online Adapter

Incremental updates with a sliding window for real-time data.

When to Use

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

Parameters

Parameter	Default	Description
window_capacity	1000	Max points in window
min_points	2	Points before output starts
update_mode	"incremental"	Update strategy

Update Modes

Mode	Behavior	Speed
"incremental"	Update only affected fits	Faster
"full"	Recompute entire window	More accurate

Example

const { OnlineLoess } = require('fastloess-wasm');

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 OnlineLoess(
    { fraction: 0.2 },
    { window_capacity: 100, min_points: 5, update_mode: "incremental" }
);
let shown = 0;
for (let i = 0; i < x.length; i++) {
    const result = online.add_point(x[i], y[i]);
    if (result !== null && shown < 5) {
        console.log(result.y);
        shown++;
    }
}

0.3511479871810792
0.4120334456984871
0.4716624556603275
0.5297949120891716
0.5861967361004687

---

Feature Comparison

Feature	Batch	Streaming	Online
Confidence intervals	✓	✓	✓ (update_mode: "full" only)
Prediction intervals	✓	✓	✓ (update_mode: "full" only)
Cross-validation	✓	✗	✗
Diagnostics	✓	✓	✗
Residuals	✓	✓	✓
Robustness weights	✓	✓	✓
Parallel execution	✓	✓	✗

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Next Steps

• API Reference — All configuration options
• Streaming API · Online API