--- name: oraclaw-forecast description: Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference. version: 1.0.0 metadata: openclaw: requires: env: - ORACLAW_API_KEY primaryEnv: ORACLAW_API_KEY emoji: "📈" homepage: https://web-olive-one-89.vercel.app/forecast tags: - forecasting - time-series - arima - prediction - revenue - analytics - holt-winters price: 0.05 currency: USDC --- # OraClaw Forecast — Time Series Prediction for Agents You are a forecasting agent that predicts future values from historical time series using ARIMA and Holt-Winters exponential smoothing. ## When to Use This Skill Use when the user or agent needs to: - Predict next N values from a data sequence (revenue, traffic, temperature, stock prices) - Get confidence intervals on forecasts ("between $80K and $120K with 95% confidence") - Detect trends, seasonality, and level shifts - Compare ARIMA (auto-fit) vs Holt-Winters (seasonal) approaches ## Tools ### `predict_forecast` ```json { "data": [100, 121, 133, 142, 155, 163, 178, 185, 192, 205, 218, 231], "steps": 6, "method": "arima" } ``` Returns: forecast values + 95% confidence interval (lower/upper bounds). For seasonal data, use Holt-Winters: ```json { "data": [362, 385, 432, 341, 382, 409, 498, 387, 473, 513, 582, 474], "steps": 4, "method": "holt-winters", "seasonLength": 4 } ``` ## Rules 1. ARIMA auto-detects the best (p,d,q) parameters. Use for non-seasonal or weakly seasonal data. 2. Holt-Winters requires `seasonLength` (e.g., 12 for monthly data with yearly seasonality, 7 for daily with weekly). 3. Minimum 10 data points for ARIMA, 2× seasonLength for Holt-Winters. 4. Confidence intervals widen the further you forecast — don't trust 30-step forecasts. 5. Best for: revenue forecasting, traffic prediction, demand planning, price trends. ## Pricing $0.05 per forecast. USDC on Base via x402. Free tier: 3,000 calls/month.