{"uid":"cap_oikJiLfJJ96Hm_ru7avs7","slug":"time-series-completeness-category-frequencies-iqr-outlier-flags-80a045e4","name":"Time Series Completeness — Category Frequencies + IQR Outlier Flags","description":"Time series completeness — Category frequencies + IQR outlier flags: Category frequencies + 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-16-107?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_2FR9FGsmzVikSsOu8RwSK","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) to produce category frequency distributions and IQR-based outlier flags across up to 200 rows in two independent reports.","exampleAgentPrompt":"Can you analyze my time series completeness records for the 'sensor-feed' group — I have observed_points and expected_points for each date from 2024-01-01 to 2024-01-10 — and give me category frequencies plus flag any IQR outliers in the observed_points field?","exampleUseCases":[{"title":"Audit sensor data completeness by group","prompt":"I have 90 rows of daily completeness records across three sensor groups — each row has a date, observed_points, and expected_points. Can you run category frequency analysis and IQR outlier detection on observed_points so I can see which groups and dates are anomalous?"},{"title":"Detect ETL pipeline data gaps","prompt":"My ETL pipeline logs show observed_points versus expected_points for each day across dataset groups 'raw' and 'processed' — can you check the expected_points field for IQR outliers and give me a frequency breakdown by category for about 150 rows?"},{"title":"QA dashboard for time series ingestion","prompt":"I want to QA our data ingestion: here are 200 rows with row_id, dataset_group, date, observed_points, and expected_points. Run both the category frequencies and IQR outlier flagging reports on observed_points so I can spot which groups are consistently underperforming."}],"resultDescription":"Returns two analytical reports: (1) a category frequency breakdown of the selected field (observed_points or expected_points) grouped by dataset_group, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile fence. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — minItems constraint violated","Row missing required fields (row_id, date, observed_points, expected_points) — validation error","Date field not matching YYYY-MM-DD pattern — schema rejection","More than 200 rows supplied — maxItems constraint exceeded","Non-numeric values in observed_points or expected_points when not null — type error","Invalid field enum value (not 'observed_points' or 'expected_points') — rejected"],"whenToPreferThis":"Choose this endpoint when you need a quick, cost-effective statistical summary of time series completeness data — specifically IQR outlier detection and category frequency counts — without requiring live data lookups, predictions, or professional assessments. Ideal for batch QA workflows processing up to 200 completeness records at a time where both frequency and outlier reports are needed together.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:57.429Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-category-frequencies-iqr-outlier-flags-80a045e4"}