generalized-timeseries 0.1.12

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

Generalized Timeseries

A package for timeseries 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

Install from pypi:

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

Install from github:

python -m venv venv
source venv/bin/activate
pip install git+https://github.com/garthmortensen/generalized-timeseries.git

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)

Alternatively,

python generalized_timeseries/examples/example.py

Publishing Maintenance

Reminder on how to manually push to pypi. This step, along with autodoc build, is automated with CI/CD.

Steps

1. bump version in pyproject.toml to v0.1.11

2. git add, commit:

   git add pyproject.toml
   git commit -m "Ver: bump"
   git tag v0.1.11
   git push && git push --tags
   

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

Design Decisions

Package project

This package was created in order to carve out code from a larger thesis remake project. Doing so increased modularity. pyproject.toml is used instead of setup.py to achieve a modern build process for publishing to pypi. It also supports direct pip installation via github clone.

Self documenting code

To achieve self-documenting code, docstrings are used.

Due to there being a fair amount of modules, classes and functions, type hints are used. Variable annotations (myvar: int = 5) are just too visually distracting, and are not used.

Sphinx converts all this metadata into .html and hosted on readthedocs.io.

Test covereage

Unit tests cover the majority of the codebase, to ensure code changes don't break existing functionality. Codecov.io is used to analyze code coverage.

Code quality

Codacy is used to analyze code quality, and highly any insecure programming issues.

Branching

Branches were used early on in the project for the sake of purity, but eventually pragmatism took over.

venv

Virtual environments and requirements.txt enable sandboxed development.

OS support

The code is OS-agnostic, and tested across ubuntu and mac-os. Due to the slow spin-up times of testing on a Windows build, it's been excluded from testing since v0.1.7.

CI/CD

A Github Actions workflow automates the labor involved with pytest, package build (wheels included), pypi distribution, producing sphinx autodocs, publishing docs to readthedocs.io, and publishing to docker hub.

Docker

TODO: Add Docker pull commands

In addition to venv, docker encapsulates the app and dependencies. This helps run it across different environments.

docker build -t generalized-timeseries:latest ./
docker run -it generalized-timeseries:latest /app/generalized_timeseries/examples/example.py  # run example pipeline
docker run -it --entrypoint /bin/bash generalized-timeseries:latest  # interactive shell in container

Makefile

Inspired by deepseek repos makefiles, I want to add a makefile.