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

---

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 { Lowess } = require('fastlowess');

const x = Float64Array.from({ length: 100 }, (_, i) => i * 2 * Math.PI / 99);
const y = Float64Array.from(x, xi => Math.sin(xi) + 0.1);

const model = new Lowess({
    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].toFixed(4)}, ${result.confidence_upper[50].toFixed(4)}]`);
console.log(`R2: ${result.diagnostics.r_squared.toFixed(4)}`);

95% CI at midpoint: [0.0433, 0.1038]
R2: 0.9668

---

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	chunk_size / 10	Overlap between chunks
merge_strategy	"weighted_average"	How to merge overlaps

Merge Strategies

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

Example

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

const x = Float64Array.from({ length: 100 }, (_, i) => i * 2 * Math.PI / 99);
const y = Float64Array.from(x, xi => Math.sin(xi) + 0.1);

const stream = new StreamingLowess(
    { 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].toFixed(4)}`);

Smoothed y[0]: 0.2578

---

Always call finalize()

Always call stream.finalize() after processing all chunks to retrieve buffered overlap data.

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 { OnlineLowess } = require('fastlowess');

const x = Float64Array.from({ length: 100 }, (_, i) => i * 2 * Math.PI / 99);
const y = Float64Array.from(x, xi => Math.sin(xi) + 0.1);

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

0.3511479871810792
0.4120334456984871
0.4716624556603275
0.5297949120891716
0.5861967361004687

---

Feature Comparison

Feature	Batch	Streaming	Online
Confidence intervals	✓	✗	✗
Prediction intervals	✓	✗	✗
Cross-validation	✓	✗	✗
Diagnostics	✓	✓	✗
Residuals	✓	✓	✓
Robustness weights	✓	✓	✓
Parallel execution	✓	✓	✗

---

Next Steps

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