| --- |
| 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. |
|
|
