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td-stationarity-test

Run ADF, KPSS, and PP stationarity tests on time series data via Teradata UAF.

7|Updated Dec 3, 2025
Search Tags:#data science#time series analysis#teradata uaf#stationarity test#adf test#kpss test
One-click install
npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-stationarity-test
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: td-stationarity-test
Source: https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-stationarity-test
Command: npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-stationarity-test

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of performing statistical tests for time series stationarity, a crucial step in time series analysis and modeling, by leveraging Teradata's Unbounded Array Framework (UAF).

Core Features & Use Cases

  • Statistical Tests: Implements Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips-Perron (PP) tests.
  • UAF Optimization: Utilizes Teradata's UAF for scalable, high-dimensional array processing.
  • Use Case: Analyze sensor data from IoT devices to determine if the underlying process generating the data is stable over time, which is essential before applying forecasting models.

Quick Start

Analyze the time series data in the table 'my_database.sensor_readings' using the timestamp column 'event_time' and value column 'temperature'.

Frequently Asked Questions about td-stationarity-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I test time series stationarity for high-dimensional data?▼

This Skill tests time series stationarity for high-dimensional data by leveraging Teradata's Unbounded Array Framework to perform scalable array processing and identify optimal UAF parameters for your datasets.

What statistical tests are available for time series stationarity in Teradata UAF?▼

Available statistical tests for time series stationarity include the Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips-Perron (PP) tests, all executed via the TD_STATIONARITY_TEST UAF function.

Can I use this Skill to analyze IoT sensor data before forecasting?▼

Yes, you can analyze IoT sensor data to determine if the underlying process is stable over time, which is an essential step to complete before applying time series forecasting models.

How do I generate SQL workflows for ADF and KPSS tests?▼

You generate SQL workflows for ADF and KPSS tests by analyzing your time series structure, which the Skill uses to automatically identify optimal UAF parameters and construct the executable queries.

Does this approach support signal processing and financial modeling applications?▼

Yes, this approach supports signal processing and financial modeling applications by analyzing time series structure to perform scalable ADF, KPSS, and PP stationarity tests on high-dimensional data.

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