{"uid":"cap_Wkv44XJSJuamD1QEAbg8_","slug":"time-series-completeness-iqr-outlier-flags-two-measure-correlation-afcfacbd","name":"Time Series Completeness IQR Outlier Flags + Two-Measure Correlation","description":"Time series completeness — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation 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-16-176?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_sWpSH2BdYZzVJ1CYrDVoi","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 rows of time series completeness data (observed vs expected points) using IQR-based outlier detection and two-measure correlation, returning two independent analytical reports.","exampleAgentPrompt":"Can you run IQR outlier detection and two-measure correlation on my completeness records? I have up to 50 rows with date, dataset_group, observed_points, and expected_points columns — I want to flag any anomalies in the observed_points field.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 90 days of pipeline completeness logs with observed_points and expected_points per day for two dataset groups. Can you flag which days are IQR outliers and show me the correlation between observed and expected counts for the observed_points field?"},{"title":"Sensor data gap detection","prompt":"My IoT sensor network records expected vs observed readings every day. Can you run IQR outlier flags and a two-measure correlation on the last 60 days of data across my sensor groups so I can spot which days had abnormal collection rates?"},{"title":"Data warehouse ingestion QA","prompt":"I want to QA our data warehouse ingestion — I have a CSV with row_id, date, dataset_group, observed_points, and expected_points for about 120 rows. Please run IQR outlier detection and correlation analysis on the expected_points field and flag anything suspicious."}],"resultDescription":"Returns two independent analytical reports: (1) IQR-based outlier flags for each row indicating whether the selected measure (observed_points or expected_points) is an outlier relative to the interquartile range, and (2) a two-measure correlation report comparing observed_points and expected_points across the submitted rows. All computation is performed locally on the supplied data 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 validation errors","Dates not matching YYYY-MM-DD pattern will fail schema validation","Too few rows for meaningful IQR computation (single-row input produces degenerate results)","Null values in numeric fields may affect outlier and correlation calculations"],"whenToPreferThis":"Choose this endpoint when you need a low-cost, self-contained statistical analysis of time series completeness data (observed vs expected points) without any external data lookups or live predictions. It is ideal for data quality audits, pipeline monitoring, and QA workflows where you already have the completeness records and need fast IQR outlier flags plus a correlation report 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":"2026-10-15T12:38:03.942Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-iqr-outlier-flags-two-measure-correlation-afcfacbd"}