Upload folder using huggingface_hub

README.md

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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}
+}
+```

arima_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9c09b471d238ae3d5c69513b32ac7cd1e78262adfd10814506c580385386bfa
+size 323437

config.json

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+{
+  "model_type": "traditional_forecasters",
+  "models": [
+    "moving_average",
+    "arima"
+  ],
+  "framework": "scikit-learn",
+  "task": "time-series-forecasting",
+  "dataset": "financial_ohlcv",
+  "metrics": {
+    "moving_average": {
+      "rmse": 2.45,
+      "mae": 1.89,
+      "mape": 1.85
+    },
+    "arima": {
+      "rmse": 2.12,
+      "mae": 1.67,
+      "mape": 1.64
+    }
+  }
+}

moving_average_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2c36b5898b40806ffbca65a5cdca167e086dc4ddcaa1600213c5625766a4bac0
+size 2090