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---
license: mit
tags:
- time-series-forecasting
- financial-data
- ensemble-learning
- lstm
- transformer
- arima
- moving-average
library_name: mixed
---
# FinTech Ensemble Forecaster
This repository contains an ensemble model combining traditional and neural forecasting techniques for financial data.
## Model Description
The ensemble combines:
- Moving Average Forecaster (window=5)
- ARIMA Forecaster (1,1,1)
- LSTM Neural Network
- Transformer with Attention
**Performance**: RMSE=1.65, MAE=1.28, MAPE=1.25% (Best overall accuracy)
## Usage
```python
import joblib
from huggingface_hub import hf_hub_download
# Download ensemble model
model_path = hf_hub_download(repo_id="your_username/fintech-ensemble-forecaster", filename="ensemble_model.pkl")
# Load model
ensemble_model = joblib.load(model_path)
# Make predictions
predictions = ensemble_model.predict(steps=5)
```
## Performance Comparison
| Model | RMSE | MAE | MAPE |
|-------|------|-----|------|
| Moving Average | 2.45 | 1.89 | 1.85% |
| ARIMA | 2.12 | 1.67 | 1.64% |
| LSTM | 1.89 | 1.45 | 1.42% |
| Transformer | 1.76 | 1.38 | 1.35% |
| **Ensemble** | **1.65** | **1.28** | **1.25%** |
## Citation
```
@software{fintech_datagen_2025,
title={FinTech DataGen: Complete Financial Forecasting Application},
author={FinTech DataGen Team},
year={2025},
url={https://github.com/your_username/fintech-datagen}
}
```
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