StationarityToolkit: Comprehensive Time Series Stationarity Analysis in Python
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911580352937984 |
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| author | Malla, Bhanu Suraj Hu, Yuqing |
| author_facet | Malla, Bhanu Suraj Hu, Yuqing |
| contents | Time-series stationarity is a property that statistical characteristics such as trend, variance, seasonality remain constant over time. It is considered fundamental to many forecasting and analysis methods. Different tests detect different types of non-stationarity: structural breaks or deterministic trends, clustered or time-dependent variance, stochastic or deterministic seasonality. A series might pass one test while failing another; single-test approaches seldom distinguish between conceptually different types of non-stationarity that require different types of tests and transformations.
`StationarityToolkit` addresses this by providing a comprehensive Python library that runs 10 statistical tests across three categories: trend (4 tests), variance (4 tests), and seasonality (2 tests). Rather than a binary stationary/non-stationary verdict, users receive detailed diagnostics with actionable notes for each detection. The toolkit automatically infers the frequency of the data provided (requires datetime index), provides clear interpretations with test statistics and p-values, and supports an iterative test-transform-retest workflow essential for real-world data sets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_08676 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StationarityToolkit: Comprehensive Time Series Stationarity Analysis in Python Malla, Bhanu Suraj Hu, Yuqing Methodology 62M10 (Primary), 62P30 (Secondary) G.3; D.2.13; I.6.4 Time-series stationarity is a property that statistical characteristics such as trend, variance, seasonality remain constant over time. It is considered fundamental to many forecasting and analysis methods. Different tests detect different types of non-stationarity: structural breaks or deterministic trends, clustered or time-dependent variance, stochastic or deterministic seasonality. A series might pass one test while failing another; single-test approaches seldom distinguish between conceptually different types of non-stationarity that require different types of tests and transformations. `StationarityToolkit` addresses this by providing a comprehensive Python library that runs 10 statistical tests across three categories: trend (4 tests), variance (4 tests), and seasonality (2 tests). Rather than a binary stationary/non-stationary verdict, users receive detailed diagnostics with actionable notes for each detection. The toolkit automatically infers the frequency of the data provided (requires datetime index), provides clear interpretations with test statistics and p-values, and supports an iterative test-transform-retest workflow essential for real-world data sets. |
| title | StationarityToolkit: Comprehensive Time Series Stationarity Analysis in Python |
| topic | Methodology 62M10 (Primary), 62P30 (Secondary) G.3; D.2.13; I.6.4 |
| url | https://arxiv.org/abs/2604.08676 |