{"uid":"cap_5By9xLIMzMs3fKCjdmopn","slug":"forecast-verification-inputs-adjacent-row-changes-iqr-outlier-flags-1c7423e8","name":"Forecast Verification Inputs — Adjacent Row Changes + IQR Outlier Flags","description":"Forecast verification inputs — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-24-157?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","forecast_value","observed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"station_group":{"type":"string","default":"","maxLength":80},"forecast_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","forecast_value":10,"observed_value":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","forecast_value":20,"observed_value":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","forecast_value":15,"observed_value":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","forecast_value":30,"observed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["forecast_value","observed_value"],"type":"string","default":"forecast_value"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ziL_HWoxIfjbVmrhX-Nsh","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Analyzes supplied forecast verification data (forecast_value and observed_value) to produce adjacent row change calculations and IQR-based outlier flags across up to 200 rows.","exampleAgentPrompt":"I have forecast verification records for two station groups from October 2026 — rows with date, forecast_value, and observed_value — can you run the adjacent row change analysis and IQR outlier flags on the forecast_value field for up to 4 rows? Row IDs are row-1 through row-4.","exampleUseCases":[{"title":"Flagging outlier forecast readings by station","prompt":"I've got a batch of weather station forecast vs observed readings for group-a and group-b across 50 dates — can you run IQR outlier detection and adjacent row change analysis on the forecast_value field so I can spot which rows have anomalous jumps?"},{"title":"Verifying observed values for data quality","prompt":"Before I publish this month's forecast verification report, can you check my 120 rows of observed_value data for IQR outliers and flag any adjacent row changes that look suspicious?"},{"title":"Detecting sudden changes in forecast series","prompt":"I need to audit the forecast_value series from my verification dataset — about 80 rows covering multiple station groups — for sudden row-to-row changes and interquartile range anomalies. Can you run both reports?"}],"resultDescription":"Returns two analytical reports for the submitted rows: (1) adjacent row change calculations showing the delta between consecutive rows for the selected field, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range bounds — all computed locally without any external lookup or live data.","failureModes":["More than 200 rows submitted — request exceeds the maximum item limit","Missing required fields (row_id, date, forecast_value, observed_value) cause validation errors","Invalid date format (non-YYYY-MM-DD) triggers schema rejection","Numeric values outside the allowed range (±1e12) cause input rejection","Empty rows array returns an error due to minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis of forecast verification datasets — specifically adjacent row delta calculations and IQR outlier detection — without requiring live weather data access, external predictions, or professional meteorological assessment. Best suited for data quality auditing and anomaly flagging on batches up to 200 rows at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:38:04.434Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-adjacent-row-changes-iqr-outlier-flags-1c7423e8"}