td-acf
OfficialAnalyze time series for patterns.
Authorteradata-labs
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the process of analyzing time series data to detect dependencies, patterns, and seasonality, which is crucial for forecasting and anomaly detection.
Core Features & Use Cases
- Auto-Correlation Analysis: Identifies how a time series is correlated with its past values at different lags using the TD_ACF function.
- Pattern Detection: Uncovers recurring patterns and cyclical behaviors within your data.
- Use Case: A retail company can use this skill to analyze daily sales data to understand seasonal trends and identify how sales on a particular day are influenced by sales on previous days, aiding in inventory management and demand forecasting.
Quick Start
Analyze the time series data in the table 'my_database.sensor_readings' which has a 'timestamp' column and a 'value' column.
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-acf Download link: https://github.com/teradata-labs/claude-cookbooks/archive/main.zip#td-acf Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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