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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)