When should I use fine-tuning instead of standard TimeGPT?

Use this skill when TimeGPT's zero-shot performance is insufficient or when your data has unique, domain-specific patterns that the base model hasn't encountered.

What data format is required for fine-tuning?

Your dataset should be a CSV containing 'unique_id' for series identifiers, 'ds' for timestamps, and 'y' for the target numeric values.

What metrics are used to evaluate the fine-tuned model?

The lab generates a metrics.json file containing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) calculated against your validation split.

Do I need a Nixtla API key to use this skill?

Yes, you must export your NIXTLA_TIMEGPT_API_KEY to your environment to authenticate the fine-tuning process.

Is there a minimum amount of data required?

For effective fine-tuning, it is recommended to provide at least 50 time series points per unique series to ensure the model can capture relevant patterns.

Nixtla TimeGPT Fine-Tuning Lab

Name: Nixtla TimeGPT Fine-Tuning Lab
Author: intent-solutions-io

byintent-solutions-io

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Ciencia de Datos y ML

Fine-tunes the TimeGPT model on custom time series data to maximize forecasting precision for domain-specific applications.

The Nixtla TimeGPT Fine-Tuning Lab enables developers to move beyond zero-shot forecasting by adapting the pre-trained TimeGPT model to their specific datasets. It provides a structured workflow for data preprocessing, training configuration, model execution, and performance evaluation. This skill is essential when dealing with specialized domains where general patterns are insufficient, allowing for higher accuracy in areas like retail demand, energy consumption, and financial forecasting by generating fine-tuned model weights and comprehensive MAE/RMSE metrics.

Características Principales

010 GitHub stars

02End-to-end fine-tuning execution for custom model adaptation

03Automated data preparation for Nixtla-compliant time series formats

04Configurable training parameters including learning rate, epochs, and batch size

05Comprehensive evaluation metrics including MAE and RMSE calculations

06Visual data verification and automated training/validation split handling

Casos de Uso

01Adapting global forecasting models to specific retail store inventory levels

02Improving energy load forecasting for smart grid management using local consumption data

03Refining financial market predictions using historical proprietary data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

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

For use in Claude.ai and ChatGPT

Download Skill