fintech-traditional-forecasters

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

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}
}