td-diff
OfficialMake time series stationary and trend-free.
Authorteradata-labs
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the challenge of analyzing time series data that exhibits trends or non-stationarity, which can hinder accurate forecasting and modeling.
Core Features & Use Cases
- Time Series Differencing: Applies differencing to remove trends and seasonality, making data stationary.
- UAF Implementation: Leverages Teradata's Unbounded Array Framework for efficient, scalable processing.
- Use Case: Analyze sensor data from IoT devices to identify anomalies or predict future behavior by first removing the underlying trend and seasonal patterns.
Quick Start
Analyze time series table: my_database.sensor_data with timestamp column and value columns.
Dependency Matrix
Required Modules
None requiredComponents
scriptsresources
💻 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-diff Download link: https://github.com/teradata-labs/claude-cookbooks/archive/main.zip#td-diff Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
Agent Skills Search Helper
Install a tiny helper to your Agent, search and equip skill from 223,000+ vetted skills library on demand.