td-cross-validation

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.