Analyze time-series data to extract trends, seasonality, and changepoints.
npx skills add https://github.com/r-irbe/proof-skills --skill math-time-series
Please help me install this Agent Skill. Skill: math-time-series Source: https://github.com/r-irbe/proof-skills/tree/main/skills/math-time-series Command: npx skills add https://github.com/r-irbe/proof-skills --skill math-time-series
Time-series analysis and temporal mathematics for analyzing evolving signals, detecting patterns, and modeling dynamics in real-valued data.
Provide a time-series dataset or question to begin analysis, with the mathematical handbook loaded for reference.
High-intent search queries and answers about installing and using this skill.
Spectral analysis decomposes time-series data into frequency components to identify periodic patterns and non-stationary signals. This Skill performs wavelet and autocorrelation analyses, extracting temporal dynamics from real-valued data for rigorous mathematical reasoning across scientific applications.
Spectral analysis decomposes time-series data into frequency components to identify periodic patterns and non-stationary signals. This Skill performs wavelet and autocorrelation analyses, extracting temporal dynamics from real-valued data for rigorous mathematical reasoning across scientific applications.
Wavelet analysis on non-stationary signals is performed using multi-scale methods to capture evolving frequency characteristics over time. This Skill applies wavelet transforms alongside autocorrelation and spectral techniques, extracting temporal dynamics from real-valued data across physics and engineering contexts.
Spectral analysis decomposes time-series data into frequency components to identify periodic patterns and non-stationary signals. This Skill performs wavelet and autocorrelation analyses, extracting temporal dynamics from real-valued data for rigorous mathematical reasoning across scientific applications.
Spectral analysis decomposes time-series data into frequency components to identify periodic patterns and non-stationary signals. This Skill performs wavelet and autocorrelation analyses, extracting temporal dynamics from real-valued data for rigorous mathematical reasoning across scientific applications.
Spectral analysis decomposes time-series data into frequency components to identify periodic patterns and non-stationary signals. This Skill performs wavelet and autocorrelation analyses, extracting temporal dynamics from real-valued data for rigorous mathematical reasoning across scientific applications.
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