{"uid":"cap_pTnmsg4XAlDae3y755u8z","slug":"time-series-completeness-audit-record-quality-string-normalisation-08574bbe","name":"Time Series Completeness Audit — Record Quality, String Normalisation & IQR Outlier Flags","description":"Time series completeness — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + 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-16-203?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_SEQ9Z2alk-jvYzRCw6Eon","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_points vs expected_points) for data quality issues, normalises strings, and flags IQR-based outliers across up to 200 rows in three independent reports.","exampleAgentPrompt":"Can you run a data quality audit on these time series completeness records — I have up to 50 rows with observed_points and expected_points by date and dataset_group — and give me the record quality report, string normalisation results, and IQR outlier flags for observed_points?","exampleUseCases":[{"title":"Daily ETL pipeline completeness check","prompt":"I have 90 rows of daily ETL completeness data with observed_points and expected_points per dataset_group — can you audit them for record quality issues, normalise the group name strings, and flag any IQR outliers in observed_points so I can catch anomalies before my morning report?"},{"title":"Sensor data coverage validation","prompt":"I'm tracking sensor data coverage across 150 dates and two sensor groups, with observed_points and expected_points for each day — please run all three quality reports on this so I can see which records have bad strings, quality issues, or outlier coverage readings."},{"title":"Data completeness monitoring for financial feeds","prompt":"We get daily financial feed completeness records and I want to validate 200 rows of observed vs expected data points — run the record quality audit, string normalisation, and IQR outlier detection on the expected_points field so I can flag anything suspicious before loading to our data warehouse."}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit identifying malformed or missing fields, (2) string normalisation results for text fields like dataset_group, and (3) IQR-based outlier flags for the selected numeric field (observed_points or expected_points). All reports are computed locally on the supplied rows with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, observed_points, expected_points) cause row-level audit failures","Invalid date format (not YYYY-MM-DD) may cause parsing errors","Null values in numeric fields reduce the effective sample for IQR calculation","Payment of 0.01 USDC not completed — request not processed"],"whenToPreferThis":"Choose this endpoint when you need a combined, cost-effective ($0.01) trio of data quality checks — record auditing, string normalisation, and IQR outlier detection — applied to time series completeness data (observed vs expected point counts) in a single call. Ideal for pipelines that need lightweight pre-load validation without external dependencies or live lookups. Not suitable for prediction, forecasting, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:59.520Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-audit-record-quality-string-normalisation-08574bbe"}