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| title: FBMC Chronos-2 Zero-Shot Forecasting | |
| emoji: ⚡ | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: jupyterlab | |
| sdk_version: "4.0.0" | |
| app_file: inference_smoke_test.ipynb | |
| pinned: false | |
| license: mit | |
| hardware: a10g-small | |
| # FBMC Flow-Based Market Coupling Forecasting | |
| Zero-shot electricity cross-border flow forecasting for 38 European FBMC borders using Amazon Chronos 2. | |
| ## 🚀 Quick Start | |
| This HuggingFace Space provides interactive Jupyter notebooks for running zero-shot forecasts on GPU. | |
| ### Available Notebooks | |
| 1. **`inference_smoke_test.ipynb`** - Quick validation (1 border × 7 days, ~1 min) | |
| 2. **`inference_full_14day.ipynb`** - Production forecast (38 borders × 14 days, ~5 min) | |
| 3. **`evaluation.ipynb`** - Performance analysis vs actuals | |
| ### How to Use | |
| 1. Open any notebook in JupyterLab | |
| 2. Run all cells (Cell → Run All) | |
| 3. View results and visualizations inline | |
| ## 📊 Dataset | |
| **Source**: [evgueni-p/fbmc-features-24month](https://huggingface.co/datasets/evgueni-p/fbmc-features-24month) | |
| - **Rows**: 17,880 hourly observations | |
| - **Date Range**: Oct 1, 2023 - Oct 14, 2025 | |
| - **Features**: 2,553 engineered features | |
| - Weather: 375 features (52 grid points) | |
| - ENTSO-E: ~1,863 features (generation, demand, prices, outages) | |
| - JAO: 276 features (CNEC binding, RAM, utilization, LTA, net positions) | |
| - Temporal: 39 features (hour, day, month, etc.) | |
| - **Targets**: 38 FBMC cross-border flows (MW) | |
| ## 🔬 Model | |
| **Amazon Chronos 2 Large** (710M parameters) | |
| - Pre-trained foundation model for time series | |
| - Zero-shot inference (no fine-tuning) | |
| - Multivariate forecasting with future covariates | |
| - Dynamic time-aware data extraction (prevents leakage) | |
| ## ⚡ Hardware | |
| **GPU**: NVIDIA A10G (24GB VRAM) | |
| - Model inference: ~5 minutes for complete 14-day forecast | |
| - Recommended for production workloads | |
| ## 📈 Performance Target | |
| **D+1 MAE Goal**: <150 MW per border | |
| This is a zero-shot baseline. Fine-tuning (Phase 2) expected to improve accuracy by 20-40%. | |
| ## 🔐 Requirements | |
| Set `HF_TOKEN` in Space secrets to access the private dataset. | |
| ## 🛠️ Technical Details | |
| ### Feature Availability Windows | |
| The system implements time-aware forecasting to prevent data leakage: | |
| - **Full-horizon D+14** (603 features): Weather, CNEC outages, LTA | |
| - **Partial D+1** (12 features): Load forecasts (masked D+2-D+14) | |
| - **Historical only** (1,899 features): Prices, generation, demand | |
| ### Dynamic Forecast System | |
| Uses `DynamicForecast` module to extract context and future covariates based on run date: | |
| - Context window: 512 hours (historical data) | |
| - Forecast horizon: 336 hours (14 days) | |
| - Automatic masking for partial availability | |
| ## 📚 Documentation | |
| - [Project Repository](https://github.com/evgspacdmy/fbmc_chronos2) | |
| - [Activity Log](https://github.com/evgspacdmy/fbmc_chronos2/blob/main/doc/activity.md) | |
| - [Feature Engineering Details](https://github.com/evgspacdmy/fbmc_chronos2/tree/main/src/feature_engineering) | |
| ## 🔄 Phase 2 Roadmap | |
| Future improvements (not included in zero-shot MVP): | |
| - Fine-tuning on FBMC data | |
| - Ensemble methods | |
| - Probabilistic forecasting | |
| - Real-time data pipeline | |
| - Production API | |
| ## 👤 Author | |
| **Evgueni Poloukarov** | |
| ## 📄 License | |
| MIT License - See LICENSE file for details | |
| --- | |
| **Last Updated**: 2025-11-14 | |
| **Version**: 1.0.0 (Zero-Shot MVP) | |