Metadata-Version: 2.4
Name: macrovolpy
Version: 0.1.0
Summary: Deep statistical forecasting and volatility learning for macro-financial time series.
Author: Khder Alakkari
License: MIT
Project-URL: Homepage, https://github.com/khderalakkari/macrovolpy
Project-URL: Repository, https://github.com/khderalakkari/macrovolpy
Project-URL: Issues, https://github.com/khderalakkari/macrovolpy/issues
Keywords: time-series,forecasting,deep-learning,volatility,CNN,LSTM,GARCH,economic-uncertainty
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Requires-Dist: scipy>=1.10
Requires-Dist: scikit-learn>=1.3
Requires-Dist: matplotlib>=3.7
Provides-Extra: deep
Requires-Dist: tensorflow>=2.13; extra == "deep"
Provides-Extra: econometrics
Requires-Dist: statsmodels>=0.14; extra == "econometrics"
Requires-Dist: arch>=6.3; extra == "econometrics"
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Dynamic: license-file

# MacroVolPy

MacroVolPy is a Python package for deep statistical forecasting and volatility learning in macro-financial time series.

It provides tools for:

- simulated macro-financial uncertainty data
- time-series transformation
- descriptive diagnostics
- CNN forecasting
- LSTM forecasting
- conditional mean-variance volatility learning
- Student-t likelihood
- prediction intervals
- ARIMA and GARCH benchmarks
- rolling-origin validation
- Differential Evolution search

## Installation

```bash
pip install macrovolpy
```

For deep learning models:

```bash
pip install macrovolpy[deep]
```

For econometric benchmarks:

```bash
pip install macrovolpy[econometrics]
```

For development:

```bash
pip install macrovolpy[dev]
```

## Quick start

```python
from macrovolpy import simulate_macro_series
from macrovolpy import DeepStatForecaster

data = simulate_macro_series(n=360)
y = data['change'].values

model = DeepStatForecaster(
    model='cnn',
    optimizer='adam',
    lookback=12,
)

model.fit(y)

forecast = model.predict(horizon=12)

print(forecast)
```

## Volatility intervals

```python
from macrovolpy import VolatilityForecaster

vol_model = VolatilityForecaster(lookback=12)

vol_model.fit(y)

intervals = vol_model.prediction_intervals(
    horizon=12,
    levels=(0.80, 0.95),
)

print(intervals)
```

## Citation

This package is based on the methodology introduced in:

Alakkari, K., Abotaleb, M., El-kenawy, E. M., and Mishra, P.

Advanced Deep Statistical Learning Approach for Forecasting Global Economic Policy Uncertainty and Volatility.

Computational Economics.

DOI: 10.1007/s10614-026-11350-7
