CRONOS™
Temporal Pattern Recognition Platform. Detects anomalies, predicts trends, and correlates signals across ANY time-series domain—without training.
What is CRONOS?
CRONOS is a real-time temporal pattern recognition platform that analyzes time-series data using pure mathematical measurement—not statistical learning.
It detects anomalies, predicts trends, and correlates signals across any domain from the very first sample. No labeled training data. No model tuning. No domain-specific configuration.
The same engine that detects bearing faults in industrial equipment also identifies seizure patterns in EEG, exoplanet transits in telescope data, and market anomalies in financial streams.
Core Philosophy
"Traditional AI looks at data and guesses what it might mean. AQEA CRONOS measures what the data actually does."
Mathematical measurement, not statistical inference.
Traditional AI vs CRONOS
| Aspect | Traditional AI/ML | AQEA CRONOS |
|---|---|---|
| Training Data | Thousands of labeled examples | Works from first sample |
| Method | Statistical learning | Mathematical measurement |
| Models | Different per domain | One engine, all domains |
| Results | Vary between runs | 100% deterministic |
| Explainability | Black box | Fully transparent |
| Speed | ~500ms (LLM) | 122 nanoseconds |
| Hardware | GPU required | Runs on ARM Cortex-M4 |
Faster
122 nanoseconds per drift analysis vs ~500ms for LLM-based approaches. Real-time processing at scale.
Training Required
No labeled data. No model tuning. No domain expertise needed. Works out-of-the-box on any time-series.
Deterministic
Same input always produces same output. No randomness. Perfect reproducibility and auditability.
Industry Applications
Industrial IoT
Bearing Fault Detection
CWRU
Medical Devices
EEG Seizure / ECG Arrhythmia
CHB-MIT / MIT-BIH
Geophysics
P-Wave / Tsunami Detection
CREW / DART
Energy & Grid
NILM Timing Accuracy
REDD
Finance
News-Market Correlation
SVB Case
Space
Exoplanet Transit
Kepler
Cybersecurity
Network Intrusion
NSL-KDD
Environmental
Air Quality Prediction
Beijing PM2.5
Performance
Hardware Requirements
Deployment Options
Edge Device
IoT sensors, embedded systems
On-Premise
Data centers, private cloud
Private Cloud
AWS/Azure/GCP isolated
SaaS API
Managed service, pay-per-use