{"uid":"cap_CsE96ScVhEofoRE9NHnXM","slug":"time-series-completeness-analyzer-exact-row-deduplication-iqr-outlier-29557058","name":"Time Series Completeness Analyzer — Exact Row Deduplication + IQR Outlier Flags","description":"Time series completeness — Exact row deduplication + IQR outlier flags: Exact row deduplication + 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-16-049?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_QU5RyATXVPAwyAUq4p86E","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 (observed_points, expected_points) by deduplicating exact duplicate rows and flagging IQR-based outliers, producing two independent analytical reports.","exampleAgentPrompt":"Can you deduplicate my time series completeness records and flag any IQR outliers in the observed_points field? I have rows with row_id, dataset_group, date, observed_points, and expected_points — about 150 rows covering multiple dataset groups.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 120 rows of daily completeness records for my ETL pipelines, each with observed_points and expected_points per dataset_group. Can you deduplicate any exact duplicate rows and flag which days look like IQR outliers in observed_points?"},{"title":"Multi-group anomaly detection","prompt":"We track data completeness for three dataset groups over the past 6 months — can you run IQR outlier detection on the expected_points field across all those rows and also remove any exact duplicates before reporting?"},{"title":"Data quality check before reporting","prompt":"Before I generate my monthly data quality report, can you check these 80 completeness records for exact duplicate rows and highlight any observed_points values that fall outside the IQR range so I know what to investigate?"}],"resultDescription":"Two independent analytical reports: one listing exact duplicate rows found in the submitted dataset, and one identifying rows where the selected field (observed_points or expected_points) is flagged as an IQR outlier. Reports are computed locally with no external lookups.","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — exceeds limit, request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Missing required fields (row_id, date, observed_points, expected_points) — schema validation failure","Null values in the selected field may affect IQR computation","Payment of 0.01 USDC not provided — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records and need both exact duplicate removal and IQR-based outlier detection in a single low-cost call. Ideal for data quality pipelines where you need two independent reports (deduplication + outlier flags) without any external data lookups or predictions. Not suitable for datasets exceeding 200 rows, for forecasting, or for outlier methods other than IQR.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:57.440Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analyzer-exact-row-deduplication-iqr-outlier-29557058"}