Code Documentation

Welcome to the technical documentation for ts-stat-tests. This section provides detailed API references for all modules, information on our quality assurance processes, and live code coverage reports.

For a detailed analysis of the underlying mathematical models and a full catalog of supported tests, please see the Algorithms Guide.

Key Features

• Unified Interface: Each statistical test is accessible through a standardised dispatcher function, providing a consistent user experience across different algorithms.
• Boolean Checkers: Each test module includes a corresponding is_<test>() function that returns a simple Boolean result, indicating whether the null hypothesis is accepted or rejected at a specified significance level.
• Comprehensive Documentation: Each module is thoroughly documented with theoretical backgrounds, algorithmic details, and practical usage examples, ensuring users can easily understand and apply the tests.
• Robust Testing Suite: The package includes an extensive suite of unit tests, achieving 100% code coverage to ensure reliability and correctness of all implementations.
• Error Handling: Standardised error handling across all modules ensures that users receive clear and informative feedback when inputs do not meet the required specifications.
• Type Safety: The use of strict type-checking with @typechecked decorators ensures that function inputs and outputs adhere to expected types, reducing runtime errors and improving code reliability.
• Extensibility: The modular design allows for easy addition of new statistical tests and algorithms in the future, facilitating ongoing development and enhancement of the library.
• Integration with Established Libraries: The package leverages well-known libraries such as statsmodels, pmdarima, tsfeatures, and antropy to implement the statistical tests, ensuring that users benefit from established and validated methodologies.
• Quality Assurance: All code passes stringent quality gates, including Pylint scores of 10/10 and zero Pyright errors, ensuring high standards of code quality and maintainability.

Modules

Module	Description
Correlation	Tests to measure autocorrelation and cross-correlation in time series data.
Regularity	Tests to measure the complexity and regularity of time series data.
Seasonality	Tests to identify seasonal patterns and measure seasonal strength in time series data.
Stability	Tests to measure the stability and lumpiness of time series data.
Stationarity	Tests to determine if a time series is stationary or possesses unit roots.
Normality	Tests to assess if the residuals of a time series model follow a normal distribution.
Linearity	Tests to evaluate linear specifications in time-series models.
Heteroscedasticity	Tests to detect heteroscedasticity in the residuals of time series models.

Testing

This package maintains 100% test coverage with comprehensive testing against:

1. Code Style: All code adheres to blackformatting, including all code chunks in docstrings using the blacken-docs.
2. Spell Checking: All documentation and code comments are spell-checked using codespell.
3. Type Safety: All code is type-checked using ty and pyright and guarded during runtype by using the typeguard library.
4. Import Sorting: All imports are sorted and managed using isort and unused imports are removed using pycln.
5. Code Quality: All code is checked for quality using pylint maintaining a score of 10/10, and checked for complexity using complexipy.
6. Docstring Quality: All docstrings are checked for style and completeness using docstring-format-checker.
7. Unit Testing: All code is unit-tested using pytest achieving 100% code coverage across the entire codebase, and including all examples in all docstrings tested using doctest.
8. Build Testing: The package is built with uv and the docs are built with mkdocs to ensure there are no build errors.

Coverage