---
license: apache-2.0
task_categories:
- time-series-forecasting
tags:
- gru
- probabilistic-forecasting
- industrial-control-systems
- uncertainty
- pytorch
---

# Chronos MicroGRU

Chronos MicroGRU predicts the next eight industrial-telemetry timestamps from a
32-step context. A compact recurrent network produces both means and Gaussian
variances for six channels.

Training uses only anomaly-free windows from devices 0-7. Variance scaling is selected
on devices 8-9, and final metrics come from unseen devices 10-11. A persistence
forecast that repeats the final observed value provides the control.

## Reproduce

```powershell
uv run python projects/edge-sentinel-ml/generate_data.py
uv run python projects/chronos-microgru/train.py
```

## Verified results

- Parameters: **8,208**
- Context: **32 timestamps**
- Forecast horizon: **8 timestamps**
- Normal training windows: **3,830**
- Held-out test windows from devices 10-11: **961**
- Model normalized RMSE: **0.2663**
- Persistence normalized RMSE: **0.3730**
- RMSE improvement: **28.61%**
- Nominal 90% interval coverage: **88.63%**
- Validation-selected variance scale: **1.05**

The recurrent model beat persistence in RMSE and MAE for all six channels. Original-unit
RMSEs were 1.008 temperature units, 0.820 pressure units, 0.087 vibration units,
0.301 current units, 0.899 flow units, and 4.042 packet-rate units.