time-series-forecasting

Route forecasting references and apply universal time-series principles to guide decisions.

3|Updated Dec 22, 2025

Search Tags:#routing #validation #forecasting #time-series #references #model-selection

Ready to run

npx skills add https://github.com/JoaquinCampo/Skills --skill time-series-forecasting

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: time-series-forecasting
Source: https://github.com/JoaquinCampo/Skills/tree/main/time-series-forecasting
Command: npx skills add https://github.com/JoaquinCampo/Skills --skill time-series-forecasting

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides routing to time-series forecasting guidance by enforcing universal principles and linking to on-demand references, helping teams write, review, and plan forecasts more effectively.

Core Features & Use Cases

Universal forecasting principles embedded in a routing framework
Scenario-driven loading of reference files from the references/ directory
Guidance on model selection, validation, backtesting, and deployment
Use Case: when starting a forecasting project, it suggests ARIMA/ETS/Theta baselines and points to advanced methods via references

Quick Start

Ask this skill to load the relevant references for your forecasting scenario and apply universal principles to guide model selection, validation, and deployment.

Frequently Asked Questions about time-series-forecasting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I select the right time-series forecasting model for my dataset?▼

Model selection for time-series forecasting depends on your data patterns. This skill applies universal principles to guide decisions, suggesting ARIMA, ETS, or Theta baselines before pointing to advanced methods via references.

What is the best way to backtest and validate a time-series forecast?▼

Backtesting a time-series forecast requires applying universal validation principles. This skill routes you to specific references that guide evaluation, validation strategies, and backtesting to ensure model reliability before deployment.

When should I use ARIMA versus ETS for time-series forecasting?▼

Choosing between ARIMA and ETS for time-series forecasting depends on data trend and seasonality. This skill provides routing to references that explain classical methods and helps you apply universal principles to pick the right baseline.

Can I get guidance on building production pipelines for ML-based time-series forecasting?▼

Building production pipelines for ML-based time-series forecasting is supported. The skill routes you to on-demand references covering deployment, ML-based approaches, and production pipeline architecture for your forecasting scenario.

Does this time-series forecasting skill require any external dependencies?▼

No external dependencies are required. The time-series forecasting skill operates independently, using a routing framework to load scenario-driven reference files and apply universal forecasting principles without needing additional packages.

Why does my time-series forecasting project need a routing framework for references?▼

A routing framework for time-series forecasting ensures universal principles are enforced consistently. It links to on-demand references for model selection and deployment, helping teams write, review, and plan forecasts more effectively.