- 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
- primaryEnv
- ORACLAW_API_KEY
- emoji
- 📈
- homepage
- https://oraclaw.dev/forecast
- tags
- 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
{
"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
}Rules
- ARIMA auto-detects the best (p,d,q) parameters. Use for non-seasonal or weakly seasonal data.
- Holt-Winters requires
seasonLength(e.g., 12 for monthly data with yearly seasonality, 7 for daily with weekly). - Minimum 10 data points for ARIMA, 2× seasonLength for Holt-Winters.
- Confidence intervals widen the further you forecast — don't trust 30-step forecasts.
- 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.