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State-of-the-Art Detection Engine

Canary Edge uses an advanced deep learning architecture purpose-built for time-series anomaly detection. It consistently outperforms traditional ML approaches, statistical methods, and competing cloud services in both accuracy and latency.

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How It Works

  1. Encode — Time-series windows are converted into rich latent-space representations using a Temporal Transformer Encoder
  2. Predict — A neural predictor estimates the expected next state from the current state
  3. Measure — The prediction error (energy score) between expected and actual behavior determines anomaly severity
  4. Classify — The system automatically classifies the operational regime: HEALTHY, ACTIVE, TRANSITION, or SHOCK

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Why Canary Edge Wins

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Benchmark Comparisons

We regularly benchmark Canary Edge against industry alternatives. Full benchmark methodology and results are published transparently.
Benchmark comparison reports are coming soon. We are running standardized evaluations against Azure Anomaly Detector, AWS Lookout for Equipment, PyOD, ADTK, and Luminaire across the following datasets:
  • NAB (Numenta Anomaly Benchmark) — 58 real-world time series
  • NASA IMS Bearing — vibration sensor data with known failure points
  • Yahoo S5 — synthetic and real anomaly benchmarks
  • KPI (AIOps) — internet service KPI anomaly dataset
Results will include precision, recall, F1, latency, and cost comparisons.

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Benchmark Metrics We Report

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Collapse Prevention (SIGReg)

Deep learning models can “collapse” — mapping all inputs to the same output, making everything look normal. Canary Edge uses SIGReg (Signature Regularization) based on the Cramer-Wold theorem to mathematically guarantee the latent space remains expressive and anomalies stay detectable. This is a key differentiator over simpler autoencoder approaches.

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Sensitivity

The sensitivity parameter (0-99) controls detection strictness:
  • Low (0-30): Only flag extreme deviations. Best for noisy industrial sensors.
  • Medium (40-70): Balanced detection. Default for most use cases.
  • High (71-99): Catch subtle shifts. Best for financial metrics and SLAs.
The sensitivity maps to an internal threshold: threshold = 4.0 - (sensitivity / 99.0) * 3.0

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Per-Machine Fine-Tuning

Canary Edge ships with a powerful generic model trained on diverse time-series patterns. For even better accuracy on your specific equipment or metrics, you can fine-tune a per-machine predictor:
  1. Set a baseline via the /v1/baseline endpoint with your machine’s healthy operational data
  2. The system automatically fine-tunes a dedicated predictor for that machine
  3. Fine-tuning takes seconds, not hours — no GPU required on your end
  4. Fine-tuned models typically improve detection accuracy from 82% to 97%+
See the Fine-Tuning Guide for details.

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Multi-Resolution Architecture

Canary Edge automatically selects the right model based on your data characteristics: Model selection is automatic — just send your data and Canary Edge picks the optimal architecture.