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Forecast Verification Inputs — Adjacent Row Changes + Secondary-Measure Weighted Mean
x402
Forecast Verification Inputs — Adjacent Row Changes + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Computes adjacent row changes and secondary-measure weighted mean analytics on up to 200 forecast verification input records (forecast_value, observed_value) using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Forecast verification inputs — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean for supplied forecast verification inputs records (forecast_value, observed_value). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports for the supplied dataset: (1) adjacent row changes — showing value differences between consecutive rows — and (2) a secondary-measure weighted mean, both computed locally on the provided forecast_value and observed_value data for up to 200 rows.
Example use cases
Weather forecast accuracy batch analysis
“I have 150 rows of daily forecast and observed temperature readings across two station groups from October 2026 — run the adjacent row changes and weighted mean reports on the forecast_value field so I can assess forecast accuracy.”
Observed value drift detection
“Can you analyze these 80 forecast verification records and compute the adjacent row changes and secondary-measure weighted mean for the observed_value field? I want to see how observed readings shift between consecutive dates.”
Multi-group forecast comparison
“I've got forecast and observed value pairs for groups A and B across 40 dates — generate both the adjacent row changes and secondary-measure weighted mean reports focusing on forecast_value so I can compare group performance.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-24-159?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two specific statistical analyses — adjacent row changes and secondary-measure weighted mean — computed locally on a batch of forecast verification records without any live data lookup, external prediction, or real-time weather service. It is ideal for post-hoc forecast accuracy analysis on datasets up to 200 rows.
Side effects
- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Missing required fields (row_id, date, forecast_value, observed_value) — validation error
- Invalid date format (not YYYY-MM-DD) — schema pattern mismatch
- field enum value not 'forecast_value' or 'observed_value' — invalid input
- Null values in both forecast_value and observed_value may reduce analytical output quality
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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