OraClaw Forecast
Skill Verified ActiveTime series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.
To equip AI agents with precise, deterministic time series forecasting abilities, moving beyond heuristic predictions to mathematically sound results.
Features
- ARIMA time series forecasting (auto-fit)
- Holt-Winters seasonal forecasting
- 95% confidence intervals for predictions
- Sub-5ms inference on the API
- MCP server, REST API, and SDK access
Use Cases
- Predicting future revenue, traffic, or prices from historical data
- Detecting trends, seasonality, and level shifts in sequential data
- Comparing different forecasting approaches (ARIMA vs. Holt-Winters)
- Obtaining statistically grounded predictions for planning and decision-making
Non-Goals
- Performing complex statistical analysis beyond forecasting
- Handling non-sequential or unstructured data
- Providing real-time, low-latency predictions for high-frequency trading
Workflow
- User or agent identifies the need for time series prediction.
- Agent invokes `predict_forecast` tool with historical data, steps, and method.
- Skill processes data using ARIMA or Holt-Winters.
- Skill returns forecast values and confidence intervals.
Practices
- Time series analysis
- Statistical modeling
- Forecasting
Prerequisites
- ORACLAW_API_KEY environment variable (for premium features)
Installation
npx skills add Whatsonyourmind/oraclawRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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