{"uid":"cap_5Bh9IF0iiFAseCnDXBE1Y","slug":"forecast-verification-inputs-string-normalisation-missing-value-fill-73f0a279","name":"Forecast Verification Inputs — String Normalisation & Missing-Value Fill","description":"Forecast verification inputs — String normalisation + Explicit missing-value fill: String normalisation + 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-24-037?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_d3r73t2ZyFHnQV_tv8pwR","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":"Normalises strings and fills missing values in forecast verification records (forecast_value, observed_value) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you clean up my forecast verification dataset — normalise the string fields and fill any missing forecast_value entries using median imputation? I have 4 rows: row-1 for group-a on 2026-10-01 with forecast 10 and observed 1, row-2 for group-b on 2026-10-02 with forecast null and observed 2, row-3 for group-a on 2026-10-03 with forecast 15 and observed null, row-4 for group-b on 2026-10-04 with forecast 30 and observed 4.","exampleUseCases":[{"title":"Impute missing forecast values before scoring","prompt":"I have 50 weather station forecast verification rows for October and some forecast_value fields are null — can you fill those missing values using the median of the available forecasts and also normalise the string fields like station_group?"},{"title":"Clean observed values with zero fill","prompt":"My observed_value column has a handful of nulls in this verification dataset of 120 rows across two station groups. Can you fill those missing observed values with zero and normalise the text fields so the data is consistent?"},{"title":"Prepare verification data for accuracy analysis","prompt":"Before I run my forecast accuracy analysis, I need to normalise strings and fill any gaps in forecast_value using mean imputation across my 80 rows of forecast-vs-observed weather data — can you do that and give me two reports?"}],"resultDescription":"Two analytical reports covering: (1) string normalisation results for text fields (station_group, date, row_id) and (2) explicit missing-value fill results for the chosen numeric field (forecast_value or observed_value) using the selected method (zero, mean, or median). Processed locally with no external lookups; covers up to 200 input rows.","failureModes":["Rows array empty or exceeds 200 items — validation error","Invalid date format (not YYYY-MM-DD) — schema rejection","row_id missing or blank — required field error","numeric values outside ±1,000,000,000,000 range — schema rejection","Unknown fill method or field enum value — schema rejection","Payment not completed — 402 response before processing begins"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local data-cleaning on forecast verification records — specifically string normalisation and missing-value imputation — without any live weather lookup, external prediction, or professional assessment. Ideal as a preprocessing step before forecast accuracy scoring, when you have up to 200 rows and want two focused reports in one call for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:37:58.897Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-string-normalisation-missing-value-fill-73f0a279"}