| # EpochsFM-TF |
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|
| A decoder-only foundation model for time series forecasting |
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| **Release Date:** September 2025 |
|
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| ## Overview |
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| EpochsFM-TF is a pretrained decoder-only foundation model designed specifically for time series forecasting. It delivers state-of-the-art performance on diverse forecasting tasks while maintaining computational efficiency. |
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| ## Key Features |
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| - **200M Parameters**: Efficient architecture optimized for forecasting |
| - **Patch-based Processing**: Handles variable-length time series contexts |
| - **Point and Quantile Forecasts**: Provides both mean predictions and uncertainty estimates |
| - **Decoder-only Architecture**: Self-attention based stack for sequence modeling |
| - **Multi-horizon Forecasting**: Predicts multiple steps ahead |
|
|
| ## Installation |
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|
| ```bash |
| pip install torch transformers |
| ``` |
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|
| ## Quick Start |
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|
| ```python |
| import torch |
| from transformers import TimesFm2_5ModelForPrediction |
| |
| model = TimesFm2_5ModelForPrediction.from_pretrained("comethrusws/epochsFM-tf") |
| model = model.to(torch.float32).eval() |
| |
| # Example time series data |
| past_values = [ |
| torch.linspace(0, 1, 100), |
| torch.sin(torch.linspace(0, 20, 67)), |
| ] |
| |
| with torch.no_grad(): |
| outputs = model(past_values=past_values, forecast_context_len=1024) |
| |
| # Mean predictions |
| print(outputs.mean_predictions.shape) |
| |
| # Full predictions (including quantiles) |
| print(outputs.full_predictions.shape) |
| ``` |
|
|
| ## Model Specifications |
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| - **Architecture**: Decoder-only transformer |
| - **Parameters**: 200M |
| - **Input**: Patch-based time series contexts |
| - **Output**: Point forecasts and quantile predictions |
| - **Context Length**: Up to 1024 time steps |
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| ## Use Cases |
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| - Demand forecasting |
| - Financial time series prediction |
| - Energy consumption forecasting |
| - Traffic and resource planning |
| - Anomaly detection preprocessing |
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| ## License |
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| This project is available under a custom license. |
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| - **Non-commercial use**: Free for personal projects, research, and educational purposes |
| - **Commercial use**: Requires explicit permission. Contact inquiry@sagea.space for licensing inquiries |
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| See LICENSE file for full terms. |
