{"uid":"cap_X6WWLiIzX2q6Gu8E2wWj2","slug":"time-series-completeness-iqr-outlier-flags-date-by-category-pivot-f170f541","name":"Time Series Completeness — IQR Outlier Flags + Date by Category Pivot","description":"Time series completeness — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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-16-178?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_i8Kc4ek6ME4bxNYivRSac","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 time series completeness records (observed vs expected data points) by running IQR-based outlier detection and a date-by-category pivot table, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run IQR outlier detection and a date-by-category pivot on my time series completeness data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04 with observed_points and expected_points, and I want to flag outliers on the observed_points field.","exampleUseCases":[{"title":"Flagging anomalous ingestion days","prompt":"I have 150 rows of daily data pipeline completeness records across five dataset groups — each row has a date, group name, observed_points, and expected_points. Can you flag any days where observed_points are IQR outliers and also pivot the data by date and group so I can see completeness patterns at a glance?"},{"title":"Monitoring sensor data gaps","prompt":"We track how many sensor readings we expect vs actually receive each day across three sensor categories. Can you take my completeness records for the past 60 days and run both an IQR outlier check on expected_points and a date-by-category summary table?"},{"title":"Auditing ETL job completeness","prompt":"I need to quality-check my ETL job completeness logs — I've got about 90 rows with dates, job group names, observed_points and expected_points. Please detect which rows are IQR outliers in observed_points and give me a pivot of observed counts by date and job group."}],"resultDescription":"Returns two analytical reports: (1) IQR outlier flags identifying which rows have values outside the interquartile range for the selected field (observed_points or expected_points), and (2) a date-by-category pivot table cross-tabulating the selected metric across dates and dataset_group values. Both reports are computed locally with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected (minItems: 1)","More than 200 rows supplied — request rejected (maxItems: 200)","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation error","Null values in both observed_points and expected_points may reduce IQR calculation reliability","Invalid field enum value (not 'observed_points' or 'expected_points') — schema rejection","Payment of 0.01 USDC not provided — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based statistical outlier detection AND a date-by-category pivot table on time series completeness data in a single cost-effective call ($0.01 USDC for both reports). Ideal for data quality monitoring pipelines, ETL observability dashboards, or any scenario where you track observed vs expected data point counts across multiple groups over time. Prefer this over general analytics tools when your data is already structured as completeness records with dataset_group, date, observed_points, and expected_points fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:33.263Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-iqr-outlier-flags-date-by-category-pivot-f170f541"}