You don't need TensorFlow.js for a trendline.

Most apps just need simple predictions: forecast next month's sales, add a trendline to a chart, smooth noisy sensor data. You don't need a 500KB neural network library for that.

micro-ml is ~56KB gzipped — 8 ML algorithms + regression + smoothing + forecasting. All in WASM. Sub-millisecond on typical datasets.

npm install micro-ml

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What Can You Do With It?

Predict Future Values

Got historical data? Predict what comes next.

// You have: sales data for 12 months
const sales = [10, 12, 15, 18, 22, 25, 28, 32, 35, 40, 45, 50];

// You want: forecast for next 3 months
const forecast = await trendForecast(sales, 3);
console.log(forecast.getForecast()); // [55, 60, 65]
console.log(forecast.direction);      // "up"

Find Trends in Data

Is your data going up, down, or flat? How strong is the trend?

const model = await linearRegressionSimple(sales);
console.log(model.slope);     // 3.7 (growing by ~3.7 per month)
console.log(model.rSquared);  // 0.98 (98% confidence - strong trend)

Smooth Noisy Data

Sensor readings jumping around? Stock prices too volatile? Smooth them out.

// Raw sensor data (noisy)
const readings = [22.1, 25.3, 21.8, 24.9, 23.2, 26.1, 22.5, ...];

// Smoothed (removes noise, shows real trend)
const smooth = await ema(readings, 5);

Fit Curves to Data

Data doesn't follow a straight line? Fit a curve instead.

// Exponential growth (bacteria, viral spread, compound interest)
const expModel = await exponentialRegression(time, population);
console.log(expModel.doublingTime()); // "Population doubles every 3.2 days"

// Polynomial curve (projectile motion, diminishing returns)
const polyModel = await polynomialRegression(x, y, { degree: 2 });

Classify Data

Got labelled data? Train a classifier.

import { knnClassifier, logisticRegression } from 'micro-ml';

// kNN — simple, no training
const knn = await knnClassifier(trainingData, labels, { k: 5 });
knn.predict([[1.5, 2.0]]); // [0] or [1]

// Logistic Regression — fast, probabilistic
const lr = await logisticRegression(data, labels, { maxIterations: 200 });
lr.predictProba([[1.5, 2.0]]); // [0.87]

Cluster Data

Find natural groups without labels.

import { kmeans, dbscan } from 'micro-ml';

// k-Means — you know how many clusters
const km = await kmeans(points, { k: 3 });
km.getCentroids();    // [[x,y], [x,y], [x,y]]
km.getAssignments();  // [0, 1, 2, 0, 1, ...]

// DBSCAN — discovers clusters + noise automatically
const db = await dbscan(points, { eps: 0.5, minPoints: 4 });
db.nClusters; // 3
db.nNoise;    // 12

Reduce Dimensions

Visualise high-dimensional data in 2D.

import { pca } from 'micro-ml';

// 50-dimensional data → 2 components
const result = await pca(data, { nComponents: 2 });
result.getExplainedVarianceRatio(); // [0.85, 0.10] (95% variance kept)
result.getTransformed();            // [[x,y], [x,y], ...]

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When to Use Which Function?

Your Data Looks Like	Use This	Example
Straight line trend	linearRegression	Stock price over time
Curved line	polynomialRegression	Ball trajectory, learning curves
Exponential growth	exponentialRegression	Bacteria growth, viral spread
Logarithmic (fast then slow)	logarithmicRegression	Learning a skill, diminishing returns
Noisy/jumpy data	ema or sma	Sensor readings, stock prices
Need future predictions	trendForecast	Sales forecast, weight loss goal
Find peaks/valleys	findPeaks / findTroughs	Detect anomalies, buy/sell signals
Group similar items	kmeans	Customer segments, image colours
Classify new items	knnClassifier	Spam detection, image recognition
Binary yes/no	logisticRegression	Churn prediction, fraud detection
Find clusters + outliers	dbscan	Anomaly detection, geo clustering
Decision rules	decisionTree	Loan approval, feature importance
Reduce dimensions	pca	Visualisation, feature extraction
Seasonal patterns	detectSeasonality	Monthly sales cycles, weekly patterns

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Real-World Use Cases

1. Sales Forecasting

Problem: "How much will we sell next quarter?"

import { trendForecast, linearRegressionSimple } from 'micro-ml';

const monthlySales = [42000, 45000, 48000, 52000, 55000, 58000];

// Analyze trend
const model = await linearRegressionSimple(monthlySales);
console.log(`Growing by $${model.slope.toFixed(0)}/month`);

// Forecast next 3 months
const forecast = await trendForecast(monthlySales, 3);
console.log('Next 3 months:', forecast.getForecast());
// → [61000, 64000, 67000]

2. Stock/Crypto Trendlines

Problem: "Is this stock trending up or down? Add a trendline to my chart."

import { linearRegression, ema } from 'micro-ml';

const days = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
const prices = [150, 152, 149, 155, 158, 156, 160, 163, 161, 165];

// Fit trendline
const trend = await linearRegression(days, prices);
const trendlinePoints = trend.predict(days);
// → Draw this as a line on your chart

// Add moving average (smoothed price)
const smoothPrices = await ema(prices, 3);
// → Draw this as another line

3. Weight Loss Prediction

Problem: "When will I reach my goal weight?"

import { linearRegressionSimple } from 'micro-ml';

const weeklyWeights = [200, 198, 196.5, 195, 193, 191.5]; // lbs
const goalWeight = 175;

const model = await linearRegressionSimple(weeklyWeights);
const lossPerWeek = Math.abs(model.slope); // 1.5 lbs/week

const currentWeight = weeklyWeights[weeklyWeights.length - 1];
const weeksToGoal = (currentWeight - goalWeight) / lossPerWeek;

console.log(`Losing ${lossPerWeek.toFixed(1)} lbs/week`);
console.log(`Goal in ${Math.ceil(weeksToGoal)} weeks`);
// → "Losing 1.5 lbs/week, Goal in 11 weeks"

4. IoT Sensor Smoothing

Problem: "Temperature sensor is noisy, I want a stable reading."

import { ema, exponentialSmoothing } from 'micro-ml';

// Raw readings jump around: 22.1, 25.3, 21.8, 24.9, ...
const rawTemperature = getSensorReadings();

// Smoothed readings: 22.5, 23.1, 22.8, 23.2, ...
const smoothed = await ema(rawTemperature, 5);

// Display the last smoothed value
displayTemperature(smoothed[smoothed.length - 1]);

5. Growth Rate Analysis

Problem: "How fast is our user base growing? When will we hit 1 million?"

import { exponentialRegression } from 'micro-ml';

const months = [1, 2, 3, 4, 5, 6];
const users = [1000, 1500, 2200, 3300, 5000, 7500];

const model = await exponentialRegression(months, users);

console.log(`Doubling every ${model.doublingTime().toFixed(1)} months`);
// → "Doubling every 1.4 months"

// When will we hit 1 million?
// Solve: 1000000 = a * e^(b*t)
const monthsToMillion = Math.log(1000000 / model.a) / model.b;
console.log(`1M users in ${monthsToMillion.toFixed(0)} months`);

6. Detecting Anomalies

Problem: "Alert me when sensor readings spike."

import { findPeaks, ema } from 'micro-ml';

const readings = [...sensorData];

// Find all spike indices
const spikes = await findPeaks(readings);

// Alert if recent spike
if (spikes.includes(readings.length - 1)) {
  alert('Anomaly detected!');
}

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Installation

npm install micro-ml

Quick Start

import { linearRegression, trendForecast, ema } from 'micro-ml';

// Fit a line to data
const model = await linearRegression([1,2,3,4,5], [2,4,6,8,10]);
console.log(model.slope);        // 2
console.log(model.predict([6])); // [12]

// Forecast future values
const forecast = await trendForecast([10,20,30,40,50], 3);
console.log(forecast.getForecast()); // [60, 70, 80]

// Smooth noisy data
const smooth = await ema([10,15,12,18,14,20], 3);

Browser Usage

<script type="module">
  import { linearRegression } from 'https://esm.sh/micro-ml';

  const model = await linearRegression([1,2,3], [2,4,6]);
  console.log(model.slope); // 2
</script>

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API Reference

Regression (Find Patterns)

Function	What It Does	When to Use
linearRegression(x, y)	Fits straight line: y = mx + b	Steady growth/decline
linearRegressionSimple(y)	Same but x = [0,1,2,...]	Time series data
polynomialRegression(x, y, {degree})	Fits curve	Curved patterns
exponentialRegression(x, y)	Fits y = a × e^(bx)	Growth/decay
logarithmicRegression(x, y)	Fits y = a + b × ln(x)	Diminishing returns
powerRegression(x, y)	Fits y = a × x^b	Power laws

Smoothing (Remove Noise)

Function	What It Does	When to Use
sma(data, window)	Simple Moving Average	General smoothing
ema(data, window)	Exponential Moving Average	Recent values matter more
wma(data, window)	Weighted Moving Average	Balance of both
exponentialSmoothing(data, {alpha})	Single exponential smooth	Quick smoothing

Forecasting (Predict Future)

Function	What It Does	When to Use
trendForecast(data, periods)	Analyze trend + predict	Future predictions
predict(xTrain, yTrain, xNew)	One-liner predict	Quick predictions
trendLine(data, periods)	Get model + predictions	When you need both

Analysis (Understand Data)

Function	What It Does	When to Use
findPeaks(data)	Find local maxima	Detect spikes
findTroughs(data)	Find local minima	Detect dips
rateOfChange(data, periods)	% change from n ago	Growth rate
momentum(data, periods)	Difference from n ago	Trend strength

Classification (Label Data)

Function	What It Does	When to Use
knnClassifier(data, labels, {k})	k-Nearest Neighbours	Simple classification
logisticRegression(data, labels, opts)	Logistic regression	Binary classification
naiveBayes(data, labels)	Gaussian Naive Bayes	Text/feature classification
decisionTree(data, labels, {maxDepth})	CART decision tree	Interpretable rules
perceptron(data, labels, opts)	Single-layer perceptron	Linear separability

Clustering (Find Groups)

Function	What It Does	When to Use
kmeans(data, {k})	k-Means clustering	Known number of groups
dbscan(data, {eps, minPoints})	Density-based clustering	Unknown clusters + noise

Dimensionality Reduction

Function	What It Does	When to Use
pca(data, {nComponents})	Principal Component Analysis	Reduce dimensions, visualise

Seasonality

Function	What It Does	When to Use
seasonalDecompose(data, period)	Decompose trend + seasonal + residual	Understand patterns
autocorrelation(data, maxLag)	Autocorrelation function	Find repeating patterns
detectSeasonality(data)	Auto-detect period + strength	Unknown periodicity

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Model Properties

All regression models return:

model.rSquared   // 0-1, how well the model fits (1 = perfect)
model.n          // Number of data points used
model.predict(x) // Predict y values for new x values
model.toString() // Human-readable equation

Linear models also have:

model.slope      // Rate of change
model.intercept  // Y-intercept

Exponential models also have:

model.a          // Initial value
model.b          // Growth rate
model.doublingTime() // Time to double

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Performance

Benchmarked in Node.js (median of 3 runs):

Algorithm	Data Size	Time
Linear Regression	100,000 pts	0.9ms
k-Means	10,000 pts, k=5	2ms
kNN	5,000 train, predict 20	3.4ms
Logistic Regression	5,000 pts	11ms
DBSCAN	2,000 pts	12ms
Naive Bayes	10,000 pts	0.2ms
PCA	5,000 × 50 → 2	13ms
Perceptron	10,000 pts	0.1ms
Seasonal Decompose	1,000 pts	0.02ms
SMA/EMA/WMA	100,000 pts	3ms

For very large datasets, use Web Workers:

import { createWorker } from 'micro-ml/worker';

const ml = createWorker();
const model = await ml.linearRegression(hugeX, hugeY); // Non-blocking
ml.terminate();

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Comparison

Library	Size (gzip)	Speed
micro-ml	~56KB	Fastest (WASM)
TensorFlow.js	500KB+	Slow
ml.js	150KB	Medium
simple-statistics	30KB	Pure JS, slower

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Links

• Documentation & Live Demos
• npm package
• GitHub

License

MIT