Reduce false alarms and catch what you're missing - with labeled synthetic time-series data
Your models don't fail because of generic data problems. They fail because rare patterns — anomaly spikes, seasonal drifts, silent sensor failures — are barely represented in your training set. Rockfish generates labeled time-series data that fills those exact gaps.
Your training data doesn't reflect what happens at 3 AM in production today
Time-series models break on rare events — anomaly spikes, sensor dropouts, correlated failures across services. These patterns are underrepresented in your historical data, so your models never see them during training.
The result: missed detections, alert storms, and manual investigation time your team can't afford.
- Rare events (spikes, flatlines, drifts) missing from training sets
- Labeled anomaly examples too scarce to generalize from
- False-positive rates stay high even after retraining
- Slow iteration cycles because augmentation is manual and time-consuming
From your production data to
a labeled training dataset — in four steps
Rockfish connects to your existing data pipeline.
No infrastructure changes, no manual labeling sprint.
Step 1
Connect your data
Point Rockfish at a slice of your production time-series data — a day, a week, a rolling window. No full export required.
Step 2
Define your patterns
Specify what rare events matter — spike types, drift magnitudes, sensor failure modes. Rockfish learns your domain's statistical fingerprint.
Step 3
Generate labeled data
Rockfish synthesizes new time-series samples with injected, labeled events — statistically realistic, never seen in production.
Step 4
Export & Train
Drop the dataset directly into your existing ML pipeline. Compatible with PyTorch, TensorFlow, and standard MLOps toolchains.
The rare events that break your models don't wait for you to label more data
With Rockfish, you can generate realistic, labeled time-series training data for any pattern -
before it shows up in production.