How does it help with model selection?

It provides a structured breakdown of which model won on the most series, interprets the consistency of errors, and recommends a production baseline based on empirical performance.

What metrics does the Nixtla Baseline Review skill analyze?

The skill analyzes standard forecasting metrics including sMAPE (Symmetric Mean Absolute Percentage Error) and MASE (Mean Absolute Scaled Error) to determine model accuracy.

Which forecasting models are supported for comparison?

It compares Nixtla's statsforecast models, specifically AutoETS, AutoTheta, and SeasonalNaive, identifying which performs best on your specific data.

Does this skill require existing results files?

Yes, you must have previously run a Nixtla baseline workflow (like nixtla-baseline-m4) to generate the CSV results files required for analysis.

Nixtla Baseline Review

Name: Nixtla Baseline Review
Author: intent-solutions-io

byintent-solutions-io

数据科学与机器学习

Analyzes and interprets Nixtla time-series forecasting benchmarks to provide data-driven model recommendations and metric insights.

关于

The Nixtla Baseline Review skill enables Claude to parse and evaluate forecasting results generated by Nixtla's statsforecast library, specifically those using M4 or custom benchmark datasets. It automates the calculation of summary statistics for models like AutoETS, AutoTheta, and SeasonalNaive, offering clear interpretations of sMAPE and MASE metrics. By identifying performance patterns and winning models across different series, this skill helps data scientists and developers quickly transition from raw experimental data to actionable production model selections.

主要功能

Automated interpretation of sMAPE and MASE metrics
Integration with TimeGPT comparison data for advanced model evaluation
0 GitHub stars
Generation of statistical summaries including mean, median, and series-level win rates
Side-by-side performance comparisons between AutoETS, AutoTheta, and SeasonalNaive
Detection of series-specific anomalies and forecasting failure cases

使用场景

Explaining complex forecasting error metrics to non-technical stakeholders
Drafting GitHub issues or benchmark reports to document experiment results
Selecting the optimal baseline model for specific time-series datasets

关于

主要功能

Automated interpretation of sMAPE and MASE metrics
Integration with TimeGPT comparison data for advanced model evaluation
0 GitHub stars
Generation of statistical summaries including mean, median, and series-level win rates
Side-by-side performance comparisons between AutoETS, AutoTheta, and SeasonalNaive
Detection of series-specific anomalies and forecasting failure cases

使用场景

Explaining complex forecasting error metrics to non-technical stakeholders
Drafting GitHub issues or benchmark reports to document experiment results
Selecting the optimal baseline model for specific time-series datasets