{"uid":"cap_a945kF0DlGkNPTKc_trl9","slug":"forecast-verification-inputs-numeric-row-ordering-explicit-missing-13aa8cc1","name":"Forecast Verification Inputs — Numeric Row Ordering + Explicit Missing-Value Fill","description":"Forecast verification inputs — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill 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-24-124?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_aczvQcgXN7dr3odrxmELz","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":"Sorts forecast verification rows numerically and fills missing forecast_value or observed_value fields using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of forecast verification data with some missing forecast_value entries — can you sort them numerically by row and fill in the missing forecast_value fields using median imputation, then give me the two analytical reports?","exampleUseCases":[{"title":"Impute missing observed values for weather station data","prompt":"I've got a batch of weather station forecast verification records — some observed_value fields are null. Can you sort the rows numerically and fill those missing observed_values using the mean, then return the two reports? I have about 50 rows across two station groups."},{"title":"Zero-fill missing forecasts before accuracy scoring","prompt":"Before I run my forecast accuracy scoring, I need to fill any missing forecast_value entries with zero and make sure my 120 rows are in numeric order. Can you process these forecast verification inputs and give me the two analytical reports?"},{"title":"Clean forecast verification export for model training","prompt":"I'm preparing a forecast verification dataset for model training — there are some nulls in the forecast_value column. Please sort all 80 rows numerically and impute the missing forecast_values using the median method, and give me both reports."}],"resultDescription":"Returns two analytical reports on the supplied forecast verification rows: one covering the numeric row ordering result and one covering the explicit missing-value fill outcome, applied to the specified field (forecast_value or observed_value) using the chosen method (zero, mean, or median). No live data is fetched or predicted.","failureModes":["Rows array empty or missing — validation error returned","More than 200 rows supplied — rejected with max-items error","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Unsupported fill method or field enum value — schema validation error","All values already present (no nulls) — fill step is a no-op, still returns reports","Numeric values outside ±1,000,000,000,000 range — rejected by min/max constraint"],"whenToPreferThis":"Choose this endpoint when you need to (1) numerically sort forecast verification rows by row_id and (2) impute missing forecast_value or observed_value entries using zero, mean, or median — all in one step with two bundled analytical reports, for up to 200 rows at $0.01 USDC. Prefer this over general-purpose data tools when your data is already in the forecast-verification schema (row_id, station_group, date, forecast_value, observed_value) and you want both ordering and imputation handled together without calling an external prediction or live weather service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:37:24.738Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-numeric-row-ordering-explicit-missing-13aa8cc1"}