{"uid":"cap_veHSIUXjrVTg309OKJnPw","slug":"time-series-completeness-record-quality-audit-string-normalisation-e8ad85ae","name":"Time Series Completeness — Record Quality Audit + String Normalisation + Missing-Value Fill","description":"Time series completeness — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-208?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"},"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_DpXgipkptQCnkIjtcJYFJ","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, normalises, and fills missing values in time series completeness records (observed_points vs expected_points) across up to 200 rows, producing 3 independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and median missing-value fill on my time series completeness data? I have about 50 rows with dataset_group, date, observed_points, and expected_points — use median imputation on the observed_points field.","exampleUseCases":[{"title":"IoT sensor completeness audit","prompt":"I have daily IoT sensor readings for the past 3 months with observed_points and expected_points per device group. Can you audit the record quality, normalise the string fields, and fill any missing observed_points using median imputation across all 90 rows?"},{"title":"Data pipeline gap detection","prompt":"My ETL pipeline logs show completeness records for 5 dataset groups over 40 days — some observed_points values are null. Please run a quality audit and fill those missing values using the mean method so I can see what was imputed."},{"title":"Monthly reporting data cleanup","prompt":"Before I generate my monthly analytics report, can you audit these 120 rows of completeness records for data quality issues, normalise the group name strings, and fill any missing expected_points values using zero-fill?"}],"resultDescription":"Returns 3 independent analytical reports per call: (1) a record quality audit flagging anomalies and invalid entries, (2) a string normalisation report for text fields like dataset_group, and (3) an explicit missing-value fill report showing imputed values for the chosen field (observed_points or expected_points) using the selected method (zero, mean, or median). All analysis is performed locally with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, date, observed_points, expected_points) causes row-level rejection","Invalid date format (not YYYY-MM-DD) fails pattern validation","Numeric values outside the allowed range (-1e12 to 1e12) are rejected","Selecting an invalid imputation method enum value returns a schema error"],"whenToPreferThis":"Choose this endpoint when you need a combined, cost-efficient batch operation that simultaneously audits record quality, normalises string fields, and imputes missing values in time series completeness data — all in one call for up to 200 rows. Ideal when you have observed_points vs expected_points data that needs cleaning before downstream analysis, and you want 3 structured reports without calling separate specialised services.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:06.758Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-string-normalisation-e8ad85ae"}