td-stationarity-test
OfficialTest time series stationarity with UAF.
Data & Analytics#data science#time series analysis#teradata uaf#stationarity test#adf test#kpss test
Authorteradata-labs
Version1.0.0
Installs0
System Documentation
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'.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: td-stationarity-test Download link: https://github.com/teradata-labs/claude-cookbooks/archive/main.zip#td-stationarity-test Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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