StationarityToolkit: Comprehensive Time Series Stationarity Analysis in Python

2026-04-09

AI Summary

Traditional stationarity tests for time series typically rely on binary decisions, which fail to distinguish among different types of nonstationarity—such as trends, variance changes, and seasonality—leading to ambiguous diagnostics and unclear preprocessing strategies. This work proposes the first Python toolkit that systematically integrates multidimensional stationarity assessments, combining ten statistical tests (four for trend, four for variance, and two for seasonality). The framework supports automatic frequency inference, an iterative “test–transform–retest” workflow, and comprehensive result visualization. By moving beyond a simple stationary/nonstationary dichotomy, it delivers fine-grained diagnostic classifications and tailored preprocessing recommendations, substantially enhancing the scientific rigor and efficiency of time series preprocessing in real-world modeling scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• time series stationarity
• non-stationarity
• statistical tests
• trend
• seasonality

Innovation

• time series stationarity
• comprehensive statistical testing
• trend-variance-seasonality decomposition
• iterative test-transform-retest workflow
• automated frequency inference