{"uid":"cap_LjnENUqV-k-VbI2t-2sZG","slug":"forecast-verification-inputs-record-quality-audit-two-measure-4c93ac01","name":"Forecast Verification Inputs — Record Quality Audit + Two-Measure Correlation","description":"Forecast verification inputs — Record quality audit + Two-measure correlation: Record quality audit + Two-measure correlation 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-24-015?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}}},"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_9zjZEaQhUSGWisSsRvAcx","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":"Audits data quality and computes two-measure correlation for up to 200 forecast vs. observed value records, returning two analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and two-measure correlation on my forecast verification data? I have rows with station groups, dates, forecast values, and observed values — here are about 50 records I want checked for data quality issues and correlation between the forecast and observed columns.","exampleUseCases":[{"title":"Weather station forecast accuracy check","prompt":"I have 120 rows of daily weather forecast and observed temperature data from two station groups across October 2026 — can you audit the record quality and compute the correlation between the forecast and observed values for me?"},{"title":"Pre-analysis data validation for model output","prompt":"Before I run my analysis, can you check these 80 forecast-vs-observed records for any data quality problems and also tell me how correlated the forecast_value and observed_value columns are?"},{"title":"Identifying bad rows in verification dataset","prompt":"I've got a set of forecast verification inputs with row IDs, dates, station groups, and both forecast and observed values — run a quality audit to flag any bad records and give me the two-measure correlation report on up to 200 rows."}],"resultDescription":"Returns two reports: (1) a record quality audit identifying data issues such as missing values, malformed entries, or anomalies across the submitted rows, and (2) a two-measure correlation analysis quantifying the statistical relationship between forecast_value and observed_value columns — all computed locally without any external lookup or live data.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, date, forecast_value, observed_value) causes row-level rejection","Malformed date strings not matching YYYY-MM-DD pattern cause validation failure","All-null forecast_value or observed_value columns may prevent meaningful correlation computation","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need both a data quality audit and a correlation analysis on forecast vs. observed value pairs in a single call, without requiring live external data access or professional meteorological assessment. It is well-suited for pre-analysis validation workflows where you want to catch record issues and measure forecast-observation alignment on batches of up to 200 rows at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:34:29.351Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-record-quality-audit-two-measure-4c93ac01"}