td-seasonal-decompose
OfficialUncover hidden patterns in your time series data.
Authorteradata-labs
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the complex process of decomposing time series data to reveal underlying seasonal patterns, trends, and cyclical components, enabling deeper insights and more accurate forecasting.
Core Features & Use Cases
- Seasonal Pattern Decomposition: Breaks down time series data into its constituent trend, seasonal, and residual elements using Teradata's Unbounded Array Framework (UAF).
- Scalable Time Series Analysis: Handles large datasets for applications like sales forecasting, IoT sensor analysis, and financial modeling.
- Use Case: Analyze monthly sales data to identify seasonal peaks and troughs, understand long-term growth trends, and forecast future sales with greater accuracy.
Quick Start
Analyze the time series data in the table 'my_database.sales_data' with timestamp column 'sale_date' and value column 'amount_sold'.
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-seasonal-decompose Download link: https://github.com/teradata-labs/claude-cookbooks/archive/main.zip#td-seasonal-decompose Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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