# EpochsFM-TF

A decoder-only foundation model for time series forecasting

**Release Date:** September 2025

## Overview

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.

## Key Features

- **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

```bash
pip install torch transformers
```

## Quick Start

```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

- **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

## Use Cases

- Demand forecasting
- Financial time series prediction
- Energy consumption forecasting
- Traffic and resource planning
- Anomaly detection preprocessing

## License

This project is available under a custom license.

- **Non-commercial use**: Free for personal projects, research, and educational purposes
- **Commercial use**: Requires explicit permission. Contact inquiry@sagea.space for licensing inquiries

See LICENSE file for full terms.