Agent Skills › Zafer-Liu/Data-Analysis-Agent › sarima

sarima

GitHub

用于季节性时间序列预测的SARIMA建模Skill。通过识别数据模式、执行季节模型验证与计算，输出参数、误差及预测区间，确保在数据充足时提供可靠的时序分析结果。

skills/sarima/SKILL.md Zafer-Liu/Data-Analysis-Agent

Trigger Scenarios

需要进行季节性时间序列预测需要评估时间数据的频率和周期完整性

Install

npx skills add Zafer-Liu/Data-Analysis-Agent --skill sarima -g -y

More Options

Use without installing

npx skills use Zafer-Liu/Data-Analysis-Agent@sarima

指定 Agent (Claude Code)

npx skills add Zafer-Liu/Data-Analysis-Agent --skill sarima -a claude-code -g -y

安装 repo 全部 skill

npx skills add Zafer-Liu/Data-Analysis-Agent --all -g -y

预览 repo 内 skill

npx skills add Zafer-Liu/Data-Analysis-Agent --list

SKILL.md

Frontmatter

{
    "icon": "🌊",
    "name": "sarima",
    "description": "使用 SARIMA 建模季节性时间序列预测（forecast）",
    "allowedTools": [
        "get_schema",
        "query_data",
        "run_analysis",
        "generate_chart"
    ]
}

SARIMA 预测

确认时间频率和季节周期，检查数据长度能否覆盖足够周期。执行季节模型与时间验证，报告参数、误差、预测区间和季节模式；数据不足时不要强行拟合。

Tool routing

Use get_schema to identify the time column, target column, seasonal frequency, and source table.
Use query_data only to verify sorted frequency, missing periods, and enough seasonal cycles.
Use run_analysis with analysis_name="Time_Series_SARIMA" for the actual forecast computation.
Use generate_chart on forecast or seasonal diagnostic result tables after run_analysis succeeds.