forecast

Forecast numeric time series data with R's auto.arima algorithm.

2|Updated Oct 30, 2025

Search Tags:#time series #data analysis #statistics #forecasting #r #arima

Ready to run

npx skills add https://github.com/zachmayer/skills --skill forecast-zachmayer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: forecast
Source: https://github.com/zachmayer/skills/tree/main/.claude/skills/forecast
Command: npx skills add https://github.com/zachmayer/skills --skill forecast-zachmayer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires forecast.

What problem does it solve?

This skill automates the complex process of statistical time series forecasting, allowing users to generate accurate predictions and confidence intervals without manual R programming.

Core Features & Use Cases

Automated ARIMA Modeling: Uses R's forecast::auto.arima to automatically select the best model parameters for your data.
Uncertainty Quantification: Provides both point forecasts and full probability distributions for decision-making under uncertainty.
Use Case: Use this to project future sales based on historical monthly CSV data or to simulate potential outcomes for a project timeline.

Quick Start

Use the forecast skill to analyze the time series data in sales_data.csv with a monthly frequency and a 12-month horizon.

Frequently Asked Questions about forecast

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

FAQPage Schema

How do I forecast time series data from a CSV file without manual R programming?▼

This skill predicts future trends from CSV-based time series data using R's auto.arima algorithm, automatically selecting the best model parameters to generate point predictions and confidence intervals without manual R programming.

Can I generate confidence intervals for time series forecasting using auto.arima?▼

Yes, this skill provides uncertainty quantification by generating full probability distributions and confidence intervals alongside point forecasts to support decision-making under uncertainty using R's auto.arima algorithm.

Do I need the R forecast package to run ARIMA modeling on my dataset?▼

Yes, you need the R forecast package installed to execute the statistical auto.arima modeling, generate visual outputs, and simulate potential outcomes for your time series data.

How do I project future sales from historical monthly time series data?▼

You project future sales by loading historical monthly CSV data into this skill and specifying a monthly frequency with a 12-month horizon, allowing the automated ARIMA modeling to handle trend projection and seasonal analysis.

What is the best way to automate statistical time series forecasting for seasonal analysis?▼

Using a skill that applies R's auto.arima algorithm automates statistical time series forecasting for seasonal analysis by automatically identifying optimal parameters to project seasonal trends and simulate potential timeline outcomes.