菜单
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".
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | nixtla-cross-validator |
| description | 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". |
| allowed-tools | Read,Write,Bash,Glob,Grep |
| version | 1.0.0 |
Evaluates time series model performance using cross-validation.
Rigorously assesses how well a time series model generalizes to unseen data by simulating future predictions.
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.
Tools: Read, Write, Bash, Glob, Grep
Environment: NIXTLA_TIMEGPT_API_KEY (if using TimeGPT)
Packages:
pip install nixtla pandas statsforecast matplotlib
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:
python {baseDir}/scripts/load_data.py data.csv
Define parameters like window size, step size, and number of folds using the configuration script.
Script: {baseDir}/scripts/configure_cv.py
The script creates expanding window splits for cross-validation. It validates that the data is sufficient for the specified window size and number of folds.
Run the cross-validation script with your chosen model and parameters.
Script: {baseDir}/scripts/cross_validate.py
Usage:
python {baseDir}/scripts/cross_validate.py \
--input data.csv \
--model arima \
--window 20 \
--folds 3 \
--freq D
Supported models:
timegpt: TimeGPT API (requires NIXTLA_TIMEGPT_API_KEY)arima: AutoARIMA from StatsForecastets: AutoETS from StatsForecasttheta: AutoTheta from StatsForecastnaive: SeasonalNaive baselineThe script automatically calculates and outputs cross-validation metrics (MAE, RMSE) for all folds.
Error: Input file not found
Solution: Ensure the specified input CSV file exists at the given path.
Error: Invalid model name
Solution: Use a supported model name: 'timegpt', 'arima', 'ets', 'theta', 'naive'.
Error: Insufficient data for cross-validation
Solution: Increase the length of the input time series or reduce the window size.
Error: Missing required parameter
Solution: Specify all required parameters: input, model, window, folds.
Error: NIXTLA_TIMEGPT_API_KEY environment variable not set.
Solution: Set the NIXTLA_TIMEGPT_API_KEY environment variable before running the script when using TimeGPT.
Input:
unique_id,ds,y
store_1,2023-01-01,10
store_1,2023-01-02,12
store_1,2023-01-03,15
...
store_1,2023-12-31,20
Command:
python {baseDir}/scripts/cross_validate.py \
--input sales.csv \
--model timegpt \
--window 30 \
--folds 4 \
--freq D
Output:
fold,unique_id,ds,y,y_hat
1,store_1,2023-11-01,18,17.5
1,store_1,2023-11-02,20,19.2
...
Input:
unique_id,ds,y
product_1,2020-01-01,100
product_1,2020-02-01,110
...
product_1,2023-12-01,125
Command:
python {baseDir}/scripts/cross_validate.py \
--input demand.csv \
--model arima \
--window 6 \
--folds 3 \
--freq M
Output:
{
"MAE": 5.2,
"RMSE": 7.1
}
{baseDir}/scripts/ directory contains all executable codeGenerate and configure Nixtla Skills using the CLI for forecasting workflows. Use when installing or updating skills. Trigger with 'install nixtla skills' or 'update nixtla'.
Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with "benchmark models", "compare TimeGPT vs StatsForecast", or "model selection".
Research and summarize Nixtla ecosystem updates and time-series forecasting content from the web and GitHub. Use when gathering release notes, recent changes, or best-practice references. Trigger with "Nixtla updates", "what's new with TimeGPT", or "find time-series papers".
Generate production-ready TimeGPT forecasting pipeline code from requirements. Use when scaffolding a pipeline with validation, logging, visualization, and repeatable runs. Trigger with "create TimeGPT pipeline", "build TimeGPT integration", or "generate forecast code".
Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series experiments. Trigger with "baseline review", "interpret sMAPE/MASE", or "compare AutoETS vs AutoTheta".
Orchestrate 6-phase changelog generation workflow with AI synthesis, multi-source data fetching (GitHub/Slack/Git), quality validation, and automated PR creation. Use when automating release notes, weekly changelogs, or documentation updates. Trigger with "generate changelog", "weekly changelog", or "automate release notes".