|
|
---
|
|
|
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}
|
|
|
}
|
|
|
```
|
|
|
|
