SARIMA 预测
SkillAI & modelsUse SARIMA to model seasonal time series forecasting
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the SARIMA 预测 skill
What this skill tells your AI
The instructions your AI receives, as published by zafer-liu/data-analysis-agent in skills/sarima/SKILL.md and read by ahel’s review.
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_schemato identify the time column, target column, seasonal frequency, and source table. - Use
query_dataonly to verify sorted frequency, missing periods, and enough seasonal cycles. - Use
run_analysiswithanalysis_name="Time_Series_SARIMA"for the actual forecast computation. - Use
generate_charton forecast or seasonal diagnostic result tables afterrun_analysissucceeds.
Implementation reference
- Tool entry:
agent/tools/business/data.py::_tool_run_analysis - Analysis registry:
Function/Analyze/registry.py - Analysis implementation:
Function/Analyze/Time_Series_SARIMA/analyze.py - Chart implementation:
Function/Charts_generation/chart_generate.py
Signals
- GitHub stars
- 3k
- Forks
- 221
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
sarima- Source
- github.com/zafer-liu/data-analysis-agent