{"uid":"cap_aYd_-V453MgCwCt4dnxLn","slug":"time-series-completeness-duplicate-key-report-iqr-outlier-flags-fa03bcff","name":"Time Series Completeness — Duplicate Key Report + IQR Outlier Flags","description":"Time series completeness — Duplicate key report + IQR outlier flags: Duplicate key report + 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-16-065?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_17lon6w7KsOSzg4IiMobq","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":"Analyzes up to 200 time series completeness records to produce a duplicate key report and IQR-based outlier flags for observed_points or expected_points values.","exampleAgentPrompt":"Check my time series completeness records for duplicate keys and IQR outlier flags on observed_points — here are my rows: row-1 group-a 2026-10-01 observed=10 expected=1, row-2 group-b 2026-10-02 observed=20 expected=2, row-3 group-a 2026-10-03 observed=15 expected=3, row-4 group-b 2026-10-04 observed=200 expected=4.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 150 rows of daily time series completeness records across multiple dataset groups — can you run a duplicate key check and flag any IQR outliers in the observed_points field so I can spot data pipeline gaps?"},{"title":"Detecting anomalous expected point counts","prompt":"Run a duplicate key report and IQR outlier analysis on the expected_points field for my completeness records — I want to know if any expected counts look statistically unusual across these 80 date-group combinations."},{"title":"Pre-ingestion data quality check","prompt":"Before I ingest these time series completeness rows, can you scan them for duplicate row_id or date-group keys and flag any observed_points outliers using IQR? I've got about 60 records to check."}],"resultDescription":"Returns two independent analytical reports: (1) a duplicate key report identifying rows sharing the same key fields (row_id, dataset_group, date), and (2) IQR outlier flags for the selected numeric field (observed_points or expected_points), identifying rows that fall outside the interquartile range bounds. Both reports cover up to 200 input rows and are computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — validation error","Empty rows array (minItems: 1 not met) — request rejected","Payment of 0.01 USDC not provided — 402 payment required"],"whenToPreferThis":"Choose this endpoint when you need both a duplicate key report AND IQR outlier detection on time series completeness data (observed_points / expected_points) in a single $0.01 call covering up to 200 rows. It is ideal for data quality checks prior to ingestion, pipeline audits, or completeness monitoring workflows where no external data lookup or prediction is needed — just fast, local statistical analysis of supplied records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:43.258Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-duplicate-key-report-iqr-outlier-flags-fa03bcff"}