Can this skill help with data visualization?

Yes, it provides specific implementation patterns for plotting individual components, helping you visualize the underlying structure of your temporal data clearly.

Which programming languages does this skill support?

The skill primarily generates production-ready code for Python (using libraries like statsmodels and pandas) and R, which are the industry standards for statistical decomposition.

What is time series decomposition?

Time series decomposition is a statistical method used to break a series into its constituent parts: trend (long-term direction), seasonality (repeating cycles), and residuals (random noise).

How do I activate the Time Series Decomposer skill?

It activates automatically when you mention 'time series decomposer' or ask Claude for help with identifying patterns and trends within time-stamped datasets.

When should I use an additive versus a multiplicative model?

The skill provides guidance on this: use additive when the seasonal variation is constant over time, and multiplicative when the seasonal variation changes proportional to the level of the trend.

Time Series Decomposer

Name: Time Series Decomposer
Author: jeremylongshore

byjeremylongshore

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983

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数据科学与机器学习

Automates the extraction of trend, seasonal, and residual components from temporal datasets for advanced statistical analysis.

The Time Series Decomposer skill equips Claude with specialized knowledge to perform rigorous temporal data analysis. It guides users through the process of breaking down complex time-series data into its fundamental components: trend, seasonality, and noise (residuals). Whether you are analyzing financial markets, retail trends, or system logs, this skill provides production-ready code implementations for additive and multiplicative models, recommends optimal window sizes, and helps generate clear visualizations to communicate underlying data patterns. It is an essential tool for data scientists and analysts who need to isolate specific variables within historical datasets for forecasting or business intelligence.

主要功能

01Support for both additive and multiplicative decomposition models

02Generation of production-ready Python and R analysis code

03983 GitHub stars

04Expert guidance on statistical validation and window selection

05Automatic extraction of trend, seasonal, and residual components

06Integrated visualization patterns for component charting

使用场景

01Identifying seasonal sales patterns in retail business intelligence

02De-trending financial data to isolate cyclical market fluctuations

03Cleaning temporal datasets for more accurate machine learning forecasting

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills time-series-decomposer

For use in Claude.ai and ChatGPT

Download Skill

主要功能

01Support for both additive and multiplicative decomposition models

02Generation of production-ready Python and R analysis code

03983 GitHub stars

04Expert guidance on statistical validation and window selection

05Automatic extraction of trend, seasonal, and residual components

06Integrated visualization patterns for component charting

使用场景

01Identifying seasonal sales patterns in retail business intelligence

02De-trending financial data to isolate cyclical market fluctuations

03Cleaning temporal datasets for more accurate machine learning forecasting

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills time-series-decomposer

For use in Claude.ai and ChatGPT

Download Skill