{"uid":"cap_4GGEbi2vl0hO-LvaMHyt9","slug":"forecast-verification-inputs-string-normalisation-adjacent-row-changes-7669d15b","name":"Forecast Verification Inputs — String Normalisation + Adjacent Row Changes","description":"Forecast verification inputs — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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-24-029?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_oZTP4d9cvlMjX9Rnsq81D","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":"Performs string normalisation and adjacent row change analysis on forecast verification input records (forecast_value, observed_value) for up to 200 rows, returning 2 bundled analytical reports.","exampleAgentPrompt":"I have 4 rows of forecast verification data for two station groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04 and paired forecast_value and observed_value numbers. Can you run string normalisation and adjacent row change analysis on the forecast_value field and give me both reports?","exampleUseCases":[{"title":"Weather station forecast accuracy review","prompt":"I've got 150 rows of daily forecast vs observed temperature readings across several station groups from 2025-01-01 to 2025-05-31. Can you normalise the strings in the observed_value field and show me where adjacent rows change significantly?"},{"title":"Pre-pipeline data quality check","prompt":"Before I load this batch of 80 forecast verification records into our pipeline, can you run the string normalisation and adjacent row changes reports on the forecast_value column so I can catch any irregularities in the sequence?"},{"title":"Model validation dataset audit","prompt":"We're auditing 200 rows of model forecast verification inputs tagged by station group and date. Please run the string normalisation plus adjacent row change analysis on the observed_value field and return both reports."}],"resultDescription":"Two analytical reports covering the submitted forecast verification rows: (1) a string normalisation report that cleans and standardises string representations of the selected field values, and (2) an adjacent row change report that flags or quantifies value changes between consecutive rows. Reports cover up to 200 input rows and are computed locally without external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected, maxItems constraint violated","Missing required fields (row_id, date, forecast_value, observed_value) — validation error","Date field not in YYYY-MM-DD format — pattern validation failure","Invalid field selector (not 'forecast_value' or 'observed_value') — enum error","Empty rows array — minItems constraint violated","Network or payment failure — no reports returned"],"whenToPreferThis":"Choose this endpoint when you have tabular forecast verification data (forecast vs observed value pairs per station and date) and need two bundled analytical reports — string normalisation and adjacent row change detection — at low cost ($0.01 USDC for up to 200 rows). It is best suited for offline/batch data quality checks on forecast verification datasets rather than live weather queries, real-time predictions, or single-metric computations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:38:03.965Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-string-normalisation-adjacent-row-changes-7669d15b"}