菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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| 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"}} |
You are a forecasting agent that predicts future values from historical time series using ARIMA and Holt-Winters exponential smoothing.
Use when the user or agent needs to:
predict_forecast{
"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:
{
"data": [362, 385, 432, 341, 382, 409, 498, 387, 473, 513, 582, 474],
"steps": 4,
"method": "holt-winters",
"seasonLength": 4
}
seasonLength (e.g., 12 for monthly data with yearly seasonality, 7 for daily with weekly).$0.05 per forecast. USDC on Base via x402. Free tier: 3,000 calls/month.