kunal732

kunal732 / MLX-Swift-TS

Public

Run Time Series Foundation Models on Apple Silicon

29
0
100% credibility
Found Feb 26, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Swift
AI Summary

A Swift toolkit for running advanced time series prediction models directly on Apple Silicon Macs and iPhones, complete with demo apps for comparing models, monitoring systems, and forecasting weather.

How It Works

1
🔍 Discover Local AI Forecasting

You hear about a simple way to predict future trends like CPU usage or weather right on your Mac or iPhone, without needing the internet.

2
📱 Open Demo Apps

Launch ready-to-use apps like Model Arena to compare smart predictors, Toto Monitor for your computer's health, or Weather Forecast for local predictions.

3
⚡ Load Your First Predictor

Tap to grab a predictor from an online collection – it downloads smoothly and lights up green when ready to think.

4
Feed in Your Numbers
📈
Single Trend

Predict one thing, like CPU load spikes.

🔗
Multiple Trends

Watch related signals together, like speed and heat, to get smarter guesses.

5
🎯 See the Future Unfold

Hit predict and watch colorful charts show what comes next, with shaded bands for confidence.

✅ Your Predictions Are Live

Everything runs lightning-fast on your device, private and ready for your apps or dashboards.

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Star Growth

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AI-Generated Review

What is MLX-Swift-TS?

Swift SDK for running time series foundation models locally on Apple Silicon Macs and iOS devices via MLX. Converts Hugging Face checkpoints (Toto, TimesFM, Chronos, etc.) to on-device format with a Python script, then forecasts univariate or multivariate series like CPU usage or weather. Delivers point predictions, quantiles, or full mixture distributions—no servers, pure runtime environment on Apple hardware.

Why is it gaining traction?

Eight models ready out-of-box, with SPM install and Hub downloads. ModelArena demo pits them head-to-head on live metrics, scoring accuracy in real-time; TotoMonitor forecasts system load, battery, or app memory. Runs github workflows locally equivalent for time series, spotting run time anomalies without cloud latency.

Who should use this?

iOS/Mac devs building on-device monitoring apps, like battery predictors or runtime error detectors in mobile dashboards. Apple data teams prototyping forecasts for sensor data or stock trends. Devs debugging run time error 1004-style issues via anomaly detection on local hardware.

Verdict

Promising for Apple devs (solid README, demos, tests), but 10 stars and 1.0% credibility mean it's early—expect rough edges. Grab for local experiments; skip production until more adoption.

(178 words)

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