Do I need an API key to use TimeGPT?

Yes, while statistical models like AutoARIMA and SeasonalNaive work locally, you must provide an NIXTLA_API_KEY to access TimeGPT features.

How does the skill handle data preparation?

It enforces the Nixtla standard schema: 'unique_id' for series identification, 'ds' for timestamps, and 'y' for the target values, ensuring compatibility across all models.

What Python libraries are required for this skill?

You will need Python 3.8+ and should install statsforecast, mlforecast, nixtla, and utilsforecast to utilize the full range of capabilities.

Can I compare multiple forecasting models at once?

Absolutely. The skill is designed to run benchmarks that compare classical statistical baselines, ML-based models, and LLM-based forecasting simultaneously.

Nixtla Time Series Lab

Name: Nixtla Time Series Lab
Author: intent-solutions-io

byintent-solutions-io

0•

データサイエンスとML

Generates production-grade time series forecasts, model benchmarks, and predictive pipelines using the Nixtla ecosystem.

The Nixtla TimeGPT Lab skill transforms Claude into a specialized forecasting expert, automating the implementation of StatsForecast, MLForecast, and TimeGPT. It streamlines the end-to-end forecasting workflow—including data schema mapping, model selection, and performance evaluation—to ensure high-accuracy predictions. Ideal for demand planning and predictive analytics, this skill generates standardized, production-ready Python code and visual comparisons to help developers deploy state-of-the-art forecasting models with minimal manual effort.

主な機能

01Production-ready Python code generation and FastAPI service scaffolding

020 GitHub stars

03Comprehensive model benchmarking with sMAPE, MASE, and MAE metrics

04Standardized data schema mapping (unique_id, ds, y) for consistency

05Integrated visualization tools for forecast comparison and confidence intervals

06Automated forecasting using StatsForecast, MLForecast, and TimeGPT

ユースケース

01Demand planning and inventory optimization for retail or logistics

02Benchmarking classical statistical models against AI foundation models

03Rapidly prototyping and deploying scalable time series APIs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add intent-solutions-io/plugins-nixtla nixtla-timegpt-lab

For use in Claude.ai and ChatGPT

主な機能

01Production-ready Python code generation and FastAPI service scaffolding

020 GitHub stars

03Comprehensive model benchmarking with sMAPE, MASE, and MAE metrics

04Standardized data schema mapping (unique_id, ds, y) for consistency

05Integrated visualization tools for forecast comparison and confidence intervals

06Automated forecasting using StatsForecast, MLForecast, and TimeGPT

ユースケース

01Demand planning and inventory optimization for retail or logistics

02Benchmarking classical statistical models against AI foundation models

03Rapidly prototyping and deploying scalable time series APIs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add intent-solutions-io/plugins-nixtla nixtla-timegpt-lab

For use in Claude.ai and ChatGPT