{"uid":"cap_YVhH6t0KTp1K-ZHQapawm","slug":"time-series-completeness-record-quality-audit-iqr-outlier-flags-0af1a5c6","name":"Time Series Completeness — Record Quality Audit + IQR Outlier Flags","description":"Time series completeness — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags 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-16-014?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"}}},"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_KUAS1MWgV7sw6XiraitOl","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 time series completeness records (observed vs expected data points) for quality issues and flags IQR-based outliers across up to 200 rows, producing 2 independent analytical reports.","exampleAgentPrompt":"Can you audit these time series completeness records for quality issues and outliers — I have up to 50 rows with observed_points and expected_points per date and dataset group, and I want both an IQR outlier report and a record quality audit flagging anything suspicious?","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have daily completeness records for my ETL pipeline — each row has a date, dataset group, observed_points, and expected_points for the past 3 months. Can you run a quality audit and IQR outlier check on observed_points to find any dates where completeness looks off?"},{"title":"Monitoring ingestion gaps across groups","prompt":"We track expected vs observed data points per day across 5 dataset groups. Can you audit these 120 rows for record quality issues and flag any outliers in expected_points using IQR analysis so I can investigate ingestion problems?"},{"title":"Pre-release data quality validation","prompt":"Before we publish this dataset, I need to verify the completeness metrics look clean — here are 80 rows with observed and expected point counts by date and group. Please run both the quality audit and the IQR outlier detection on observed_points and tell me which rows to review."}],"resultDescription":"Returns 2 independent analytical reports: (1) a record quality audit identifying issues such as nulls, mismatches, or malformed entries in the completeness records, and (2) IQR-based outlier flags identifying rows where the selected field (observed_points or expected_points) deviates significantly from the interquartile range. Reports are computed locally with no external lookups.","failureModes":["Fewer than 1 row provided — minimum of 1 row required","More than 200 rows submitted — batch size exceeds maximum","Date field does not match YYYY-MM-DD pattern — validation error","Missing required fields (row_id, date, observed_points, expected_points) — rejected row","observed_points or expected_points out of numeric bounds — validation failure","Invalid field selector enum value — must be 'observed_points' or 'expected_points'","Payment not included or insufficient — x402 payment required at $0.01 USDC"],"whenToPreferThis":"Choose this endpoint when you need a fast, local statistical audit of time series completeness data (observed vs expected points) without sending data to external services or requiring live lookups. Ideal for data pipeline monitoring, pre-publish QA, or ingestion validation workflows where you want both a record-level quality audit and IQR-based anomaly detection in a single call for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:55.597Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-iqr-outlier-flags-0af1a5c6"}