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

sarima

GitHub

该技能用于季节性时间序列预测。通过确认频率和周期，验证数据质量后，调用SARIMA模型进行计算，并生成图表报告参数、误差及预测区间，数据不足时不强行拟合。

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

Trigger Scenarios

需要预测具有季节性特征的时间序列数据请求对历史数据进行季节性趋势分析和未来值估算

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.