{"uid":"cap_urq8whqA6jMEh3f9dMfYv","slug":"time-series-completeness-audit-missing-value-fill-a8e8ae09","name":"Time Series Completeness Audit & Missing-Value Fill","description":"Time series completeness — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied time series completeness records (observed_points, expected_points). 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-analytics-16-019?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_Xx_L_DkXHVXqkpF9LEldl","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 fills missing values in time series completeness records (observed_points, expected_points) for up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Can you run a quality audit and fill in missing observed_points values (using median fill) on my time series completeness records — I have about 50 rows with dates, dataset groups, observed_points, and expected_points that need checking?","exampleUseCases":[{"title":"IoT sensor completeness audit","prompt":"I have 120 rows of daily IoT sensor completeness records with observed_points and expected_points per dataset group — can you audit them for quality issues and fill any missing observed_points using median fill?"},{"title":"ETL pipeline gap detection","prompt":"Run a record quality audit on my ETL pipeline completeness data: I've got 80 rows covering the past quarter with observed and expected data point counts per pipeline group, and I want to know where the gaps are and get missing values filled with zeros."},{"title":"Multi-group data completeness validation","prompt":"I need two reports on my time series completeness records — one auditing quality and one filling missing expected_points using mean fill — across 3 dataset groups and about 60 daily rows from last month."}],"resultDescription":"Returns two independent reports: (1) a record quality audit highlighting anomalies, nulls, and data quality issues in the submitted completeness records, and (2) a missing-value fill report where the specified field (observed_points or expected_points) has nulls replaced using the chosen method (zero, mean, or median). All processing is local/analytical with no external lookups or predictions.","failureModes":["More than 200 rows submitted — exceeds batch limit, request will be rejected","Invalid date format (not YYYY-MM-DD) causes row validation failure","Missing required fields (row_id, date, observed_points, expected_points) causes schema validation error","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected","Invalid fill method or field enum value causes parameter error","Payment of 0.01 USDC not provided or fails — request not processed"],"whenToPreferThis":"Choose this endpoint when you need a combined quality audit and missing-value imputation report on time series completeness data (observed vs expected data points) in a single call. Ideal for data engineers or analysts who want batch processing of up to 200 rows with multiple fill strategies (zero, mean, median) and two simultaneous report types. Not suitable for live data lookup, forecasting, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:51.033Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-audit-missing-value-fill-a8e8ae09"}