{"uid":"cap_WDOId7uXAdSXaDV-TzB1c","slug":"forecast-verification-inputs-daily-sum-series-explicit-missing-value-c6822f2a","name":"Forecast Verification Inputs — Daily Sum Series + Explicit Missing-Value Fill","description":"Forecast verification inputs — Daily sum series + Explicit missing-value fill: Daily sum series + 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-24-145?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_pqvibjHh5EXAMiFxvZNRr","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":"Computes a daily sum series and fills missing values (zero, mean, or median) for forecast vs. observed value records, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 150 rows of daily forecast vs. observed weather readings for two station groups from October 2026 — can you compute the daily sum series and fill any missing forecast_value entries using the median method?","exampleUseCases":[{"title":"Weather station forecast accuracy audit","prompt":"I have daily forecast and observed precipitation records for station groups north-a and north-b covering the past 6 months, with some nulls in the observed_value column — run the daily sum series and fill those missing observed values using the mean, up to 200 rows."},{"title":"Null imputation before model retraining","prompt":"Before I retrain my forecasting model, I need to fill all missing forecast_value entries in my 80-row verification dataset using zero imputation and get the daily sum series so I can spot any anomalies by date."},{"title":"Station group daily aggregation report","prompt":"Can you take these 120 rows of forecast vs. actual temperature readings split across group-east and group-west, generate the daily sum series, and fill any gaps in forecast_value with the median — I need both reports back."}],"resultDescription":"Returns two independent analytical reports: (1) a daily sum series aggregated from the supplied forecast_value and observed_value rows, and (2) an explicit missing-value fill result for the chosen field (forecast_value or observed_value) using the specified method (zero, mean, or median). Reports cover up to 200 input rows grouped by date and station_group.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) causes validation error","Missing required fields (row_id, date, forecast_value, observed_value) cause schema rejection","Values outside ±1,000,000,000,000 range rejected","Invalid enum for method or field parameter causes error","Empty rows array rejected (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have tabular forecast vs. observed value records (up to 200 rows) that need both a daily sum aggregation and explicit missing-value imputation in a single call at low cost ($0.01 USDC). It is particularly suited for weather or time-series verification workflows where null handling and daily aggregation are needed together without an external data lookup or live prediction service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:38:48.924Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-daily-sum-series-explicit-missing-value-c6822f2a"}