td-pacf
OfficialAnalyze time series for direct lag relationships
Authorteradata-labs
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the process of performing partial auto-correlation analysis on time series data, enabling users to understand direct lag relationships and identify optimal model parameters without deep UAF expertise.
Core Features & Use Cases
- Partial Auto-Correlation: Computes the TD_PACF function to identify direct correlations between a time series and its lagged values, excluding indirect effects.
- UAF Integration: Leverages Teradata's Unbounded Array Framework (UAF) for scalable and efficient time series analysis.
- Use Case: A financial analyst can use this skill to analyze daily stock prices to understand how the price on a given day is directly influenced by prices from specific previous days, aiding in ARIMA model specification.
Quick Start
Analyze the time series table 'my_database.sensor_readings' with 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-pacf Download link: https://github.com/teradata-labs/claude-cookbooks/archive/main.zip#td-pacf Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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