OraClaw Forecast
技能 已验证 活跃AI 代理的时间序列预测。ARIMA 和 Holt-Winters 预测（含置信区间）。预测收入、流量、价格或任何序列数据。推理延迟低于 5 毫秒。
为 AI 代理提供精确、确定性的时间序列预测能力，超越启发式预测，实现数学上可靠的结果。
功能
- ARIMA 时间序列预测（自动拟合）
- Holt-Winters 季节性预测
- 预测的 95% 置信区间
- API 推理延迟低于 5 毫秒
- MCP 服务器、REST API 和 SDK 访问
使用场景
- 根据历史数据预测未来的收入、流量或价格
- 检测序列数据中的趋势、季节性和水平变化
- 比较不同的预测方法（ARIMA vs. Holt-Winters）
- 为规划和决策获取统计上合理的预测
非目标
- 执行超出预测范围的复杂统计分析
- 处理非序列或非结构化数据
- 为高频交易提供实时、低延迟预测
工作流
- 用户或代理识别出时间序列预测的需求。
- 代理调用 `predict_forecast` 工具，提供历史数据、预测步数和方法。
- 技能使用 ARIMA 或 Holt-Winters 处理数据。
- 技能返回预测值和置信区间。
实践
- 时间序列分析
- 统计建模
- 预测
先决条件
- ORACLAW_API_KEY 环境变量（用于高级功能）
安装
npx skills add Whatsonyourmind/oraclaw通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
TimesFM Forecasting
100Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Alterlab Aeon
98Part of the AlterLab Academic Skills suite. This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Oraclaw Calibrate
97AI 代理的预测质量评分。Brier 分数、对数分数和多源收敛性分析。了解您的预测是否准确以及您的数据源是否一致。
TimesFM Forecasting
96Part of the AlterLab Academic Skills suite. Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Forecast Operational Metrics
95Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.
Aeon Time Series Machine Learning
95This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.