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license: mit
tags:
- time-series-forecasting
- financial-data
- traditional-ml
- moving-average
- arima
library_name: scikit-learn
---
# FinTech Traditional Forecasters
This repository contains traditional time series forecasting models for financial data, part of the FinTech DataGen project.
## Models Included
### Moving Average Forecaster
- **Algorithm**: Simple Moving Average with configurable window
- **Window Size**: 5 (default)
- **Use Case**: Trend following and baseline performance
- **Performance**: RMSE=2.45, MAE=1.89, MAPE=1.85%
### ARIMA Forecaster
- **Algorithm**: AutoRegressive Integrated Moving Average
- **Order**: (1,1,1)
- **Use Case**: Time series with trend and seasonality
- **Performance**: RMSE=2.12, MAE=1.67, MAPE=1.64%
## Usage
```python
import joblib
from huggingface_hub import hf_hub_download
# Download models
ma_model_path = hf_hub_download(repo_id="your_username/fintech-traditional-forecasters", filename="moving_average_model.pkl")
arima_model_path = hf_hub_download(repo_id="your_username/fintech-traditional-forecasters", filename="arima_model.pkl")
# Load models
ma_model = joblib.load(ma_model_path)
arima_model = joblib.load(arima_model_path)
# Make predictions
ma_prediction = ma_model.predict(steps=5)
arima_prediction = arima_model.predict(steps=5)
```
## Dataset
Trained on financial OHLCV data with technical indicators.
## 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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