Does this skill require pre-existing data?

Yes, it requires results files (CSV) generated by a Nixtla workflow, typically located in the nixtla_baseline_m4/ directory.

Which forecasting models are compared by the Nixtla Baseline Review?

It compares SeasonalNaive, AutoETS (Exponential Smoothing), and AutoTheta models, identifying which one is most consistent and accurate for your data.

What specific metrics does this skill interpret?

The skill focuses on sMAPE (Symmetric Mean Absolute Percentage Error) to measure accuracy and MASE (Mean Absolute Scaled Error) to compare performance against a seasonal naive baseline.

How does this help with model selection?

It calculates 'Series Won' counts and consistency scores (standard deviation), providing a clear recommendation on which model should be used as the production baseline.

Nixtla Baseline Review

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

byintent-solutions-io

Data Science & ML

Analyzes and interprets time-series forecasting metrics from Nixtla baseline models to optimize model selection and performance.

About

The Nixtla Baseline Review skill provides automated analysis of time-series forecasting experiments, specifically focusing on benchmark results like the M4 dataset. It parses output files to compare the performance of AutoETS, AutoTheta, and SeasonalNaive models, offering deep dives into error metrics like sMAPE and MASE. By surfacing patterns, identifying dominant models, and providing clear statistical summaries, this skill helps data scientists and developers transition from raw experimental data to actionable production forecasting strategies.

Key Features

Comparative analysis of AutoTheta, AutoETS, and SeasonalNaive performance
Detailed interpretation of sMAPE and MASE forecasting metrics
Structured recommendations for production-ready model selection
Automated parsing of Nixtla benchmark results and CSV metrics
0 GitHub stars
Statistical summaries including mean error, median error, and win rates

Use Cases

Identifying series-specific patterns where certain models excel or fail
Explaining complex error metrics to stakeholders in plain language
Evaluating which forecasting model performed best on a specific dataset

About

Key Features

Comparative analysis of AutoTheta, AutoETS, and SeasonalNaive performance
Detailed interpretation of sMAPE and MASE forecasting metrics
Structured recommendations for production-ready model selection
Automated parsing of Nixtla benchmark results and CSV metrics
0 GitHub stars
Statistical summaries including mean error, median error, and win rates

Use Cases

Identifying series-specific patterns where certain models excel or fail
Explaining complex error metrics to stakeholders in plain language
Evaluating which forecasting model performed best on a specific dataset