{"uid":"cap_iLViURj1S-i2sZUoq0Thp","slug":"time-series-completeness-audit-record-quality-deduplication-iqr-d344202f","name":"Time Series Completeness Audit: Record Quality, Deduplication & IQR Outlier Flags","description":"Time series completeness — Record quality audit + Exact row deduplication + IQR outlier flags: Record quality audit + Exact row deduplication + IQR outlier flags 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-16-220?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_JnhdRsOVZUF_Osw0iOMAh","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":"Runs three local analytical reports on time series completeness data (observed vs expected points): record quality audit, exact row deduplication, and IQR-based outlier flagging — up to 200 rows per call.","exampleAgentPrompt":"Can you audit these time series completeness records for quality issues, exact duplicates, and IQR outliers? I have rows with dataset_group, date, observed_points, and expected_points — up to 50 rows — and I want all three reports run on the observed_points field.","exampleUseCases":[{"title":"Pipeline completeness daily audit","prompt":"I have 80 rows of daily completeness logs for my data pipelines — each row has a date, dataset_group, observed_points, and expected_points. Can you run a full quality audit, deduplication check, and IQR outlier detection on the observed_points field for me?"},{"title":"Catching duplicate records before reporting","prompt":"Before I publish my weekly completeness report, can you check this batch of 120 completeness records for exact duplicate rows? I also want a quality audit and outlier flags on expected_points while you're at it."},{"title":"Anomaly investigation in sensor coverage data","prompt":"Something looks off in my sensor coverage dataset — I have 150 rows with dates, group names, observed_points, and expected_points. Can you flag any IQR outliers in observed_points, plus run record quality and deduplication checks on the whole set?"}],"resultDescription":"Returns three independent analytical reports for the submitted rows: (1) a record quality audit identifying missing, malformed, or suspicious entries; (2) an exact row deduplication report listing identical rows; and (3) IQR-based outlier flags on the selected numeric field (observed_points or expected_points). All analysis is performed locally with no external data lookups.","failureModes":["Submitting more than 200 rows returns an error — batch must be split","Missing required fields (row_id, date, observed_points, expected_points) cause row rejection","Dates not matching YYYY-MM-DD pattern fail schema validation","Null numeric values may reduce outlier detection accuracy","Invalid enum value for 'field' parameter causes request failure"],"whenToPreferThis":"Choose this endpoint when you need a combined, affordable ($0.01 USDC) batch audit of time series completeness records covering quality, duplicates, and IQR outliers in a single call. Ideal for pre-ingestion validation pipelines, completeness monitoring workflows, or ad-hoc data quality checks on observed vs expected point counts of up to 200 rows. Not suitable for real-time streaming validation, predictive analytics, or datasets requiring external reference data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:55.861Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-audit-record-quality-deduplication-iqr-d344202f"}