generalized-timeseries 0.1.7

A package for time series data processing and modeling using ARIMA and GARCH models

Generalized Timeseries

A package for time series data processing and modeling using ARIMA and GARCH models.

Features

• Price series generation for simulation.
• Data preprocessing including missing data handling and scaling.
• Stationarity testing and transformation.
• ARIMA and GARCH models for time series forecasting.

Installation

python -m venv venv
source venv/bin/activate
pip install generalized-timeseries

Usage

from generalized_timeseries import data_generator, data_processor, stats_model

# generate price series data
price_series = data_generator.generate_price_series(length=1000)

# preprocess the data
processed_data = data_processor.preprocess_data(price_series)

# fit ARIMA model
arima_model = stats_model.fit_arima(processed_data)

# fit GARCH model
garch_model = stats_model.fit_garch(processed_data)

# forecast using ARIMA model
arima_forecast = stats_model.forecast_arima(arima_model, steps=10)

# forecast using GARCH model
garch_forecast = stats_model.forecast_garch(garch_model, steps=10)

print("ARIMA Forecast:", arima_forecast)
print("GARCH Forecast:", garch_forecast)

External resources

pypi repo

readthedocs.io

Publishing Maintenance

pypi

pip install --upgrade build
pip install --upgrade twine
python -m build  # build the package
twine check dist/  # check it works
twine upload dist/

rm -rf dist build *.egg-info # restart if needed

Publishing via Github Actions

Pypi publication occurs when pushing a tag:

git tag v0.1.7
git push origin v0.1.7