nixtla-cross-validator
Performs rigorous time series cross-validation using expanding and sliding windows. Use when needing to evaluate the performance of time series models on unseen data. Trigger with cross validate time series, evaluate forecasting model, time series backtesting.
npx skills add majiayu000/claude-skill-registry --skill nixtla-cross-validator-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cross-Validator Skill Evaluates time series model performance using cross-validation. ## Purpose Rigorously assesses how well a time series model generalizes to unseen data by simulating future predictions. ## Overview This skill automates time series cross-validation by splitting historical data into multiple training and validation sets based on expanding or sliding window techniques. It integrates with TimeGPT and StatsForecast to evaluate model performance across various time periods. It reports key accuracy metrics, helping users select the best model. ## Prerequisites **Tools**: Read, Write, Bash, Glob, Grep **Environment**: `NIXTLA_TIMEGPT_API_KEY` (if using TimeGPT) **Packages**: ```bash pip install nixtla pandas statsforecast matplotlib ``` ## Instructions ### Step 1: Prepare data Read time series data from CSV file into a pandas DataFrame using the data loader script. Script: `{baseDir}/scripts/load_data.py` The script expects a CSV file with columns: `unique_id`, `ds` (timestamp), and `y` (target value). **Example usage**: ```bash python {baseDir}/scripts/load_data.py data.csv ``` ### Step 2: Configure cross-validation Define parameters like window size, step size, and
- Purpose
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare data
- Step 2: Configure cross-validation
- Step 3: Execute cross-validation
- Step 4: Analyze results
- Output
- Error Handling
- Examples
- Example 1: Cross-validating TimeGPT on daily sales
- Example 2: Cross-validating ARIMA on monthly demand
- Resources
pip install nixtla pandas statsforecast matplotlib
python {baseDir}/scripts/load_data.py data.csv
python {baseDir}/scripts/cross_validate.py \What does the nixtla-cross-validator skill do?
Performs rigorous time series cross-validation using expanding and sliding windows. Use when needing to evaluate the performance of time series models on unseen data. Trigger with cross validate time series, evaluate forecasting model, time series backtesting.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-cross-validator-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.