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td-cross-validation

Perform time series cross-validation using Teradata UAF TD_CROSS_VALIDATION.

7|Updated Dec 3, 2025
Search Tags:#time series#forecasting#cross-validation#model validation#overfitting#teradata uaf
One-click install
npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-cross-validation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: td-cross-validation
Source: https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-cross-validation
Command: npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-cross-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of accurately validating time series models by implementing specialized cross-validation techniques that respect temporal data dependencies, preventing common pitfalls like data leakage and ensuring robust model performance assessment.

Core Features & Use Cases

  • Time Series Cross-Validation: Implements various methods like K-Fold, Rolling Origin, Blocked CV, and Expanding Window to split data for training and testing.
  • Overfitting Detection: Provides metrics to identify if a model is generalizing poorly to unseen data.
  • Parameter Optimization: Helps in selecting the best cross-validation strategy and parameters for a given time series dataset.
  • Use Case: A data scientist needs to validate a sales forecasting model. This Skill can automatically test the model's performance across different historical periods, ensuring its reliability for future predictions.

Quick Start

Use the td-cross-validation skill to analyze time series table: my_database.sales_data with timestamp column and value columns.

Frequently Asked Questions about td-cross-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent data leakage when performing cross-validation on time series data?▼

Time series cross-validation prevents data leakage by using temporal order-aware splitting methods like Blocked CV and Expanding Window. This approach ensures future data never contaminates training sets, providing robust and reliable forecasting model validation.

What's the best way to validate a forecasting model for overfitting on historical data?▼

Validating forecasting models for overfitting requires testing predictions across multiple historical time periods using cross-validation. Comparing performance metrics across these splits identifies poor generalization to unseen data, ensuring reliable future predictions.

How do I run time series cross-validation in Teradata for high-dimensional data?▼

Running time series cross-validation in Teradata uses the Unbounded Array Framework TD_CROSS_VALIDATION function for scalable analysis. It generates production-ready SQL to process high-dimensional datasets while maintaining comprehensive error handling and business-focused interpretation.

When should I use expanding window versus blocked cross-validation for time series?▼

Use expanding window cross-validation when training data volume grows incrementally over time, and blocked cross-validation to strictly separate contiguous time periods. Parameter optimization helps select the best strategy for a specific dataset to prevent temporal overlap.

Does Teradata UAF support parameter optimization for time series model validation?▼

Teradata UAF supports parameter optimization for time series model validation through the TD_CROSS_VALIDATION function. It enables selecting optimal cross-validation strategies and parameters, generating scalable SQL workflows with comprehensive error handling for production-ready analysis.

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