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Time Series in R

Manage time series structures and autocorrelation diagnostics in R.

Updated Mar 7, 2026
Search Tags:#time series#r#ts#autocorrelation#zoo#xts#data diagnostics
One-click install
npx skills add https://github.com/ntluong95/agent-skills-statistics --skill time-series-in-r
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Time Series in R
Source: https://github.com/ntluong95/agent-skills-statistics/tree/main/skills/statistical-computing/time-series
Command: npx skills add https://github.com/ntluong95/agent-skills-statistics --skill time-series-in-r

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complexities of working with time series data in R, including handling different data structures, diagnosing autocorrelation, and managing irregular time intervals, which are crucial for accurate statistical modeling.

Core Features & Use Cases

  • Data Structure Management: Supports ts, zoo, xts, and data.frame for various time series needs.
  • Autocorrelation Diagnostics: Provides tools to check for and interpret autocorrelation in model residuals using ACF and PACF plots.
  • Irregular Series Handling: Offers methods to complete missing dates and detect gaps in time series data.
  • Use Case: When analyzing daily air pollution data that has occasional missing days, this skill helps in completing the series and checking if the model residuals exhibit any remaining temporal correlation.

Quick Start

Use the Time Series in R skill to check the autocorrelation of residuals from a fitted model.

Frequently Asked Questions about Time Series in R

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

FAQPage Schema
How do I check autocorrelation in time series residuals using R?▼

To check autocorrelation in time series residuals, this Skill applies ACF and PACF diagnostics to evaluate temporal dependencies in model residuals using base R data frames and structures like ts, zoo, and xts.

What is the best way to handle missing dates in irregular time series in R?▼

Handling missing dates in irregular time series involves completing the series and detecting temporal gaps. This Skill provides methods to manage irregular intervals using zoo and xts structures for accurate statistical modeling.

Can I use this Skill to prepare data for DLNM models in R?▼

Yes, you can prepare data for DLNM models by assessing temporal dependencies and ensuring data integrity. This Skill manages time series data structures and diagnostics required before fitting distributed lag non-linear models.

Does this Skill support converting between ts, zoo, and xts data structures?▼

Yes, it supports data structure management across ts, zoo, xts, and data.frame formats. This allows you to transition between regular and irregular time series objects for various statistical computing tasks in R.

Why do I need to diagnose temporal dependencies before fitting a time series model?▼

Diagnosing temporal dependencies is required for data integrity and accurate statistical modeling. This Skill evaluates autocorrelation and irregular time intervals to prevent biased estimates in your downstream analysis.

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