{"uid":"cap_CNb2P_SuIreoNBmFpsZeQ","slug":"time-series-completeness-numeric-distribution-iqr-outlier-flags-d9e92875","name":"Time Series Completeness — Numeric Distribution + IQR Outlier Flags","description":"Time series completeness — Numeric distribution + IQR outlier flags: Numeric distribution + 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-16-080?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_Nkbp7_oD8bz61iNgnDbY4","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) and returns numeric distribution statistics plus IQR-based outlier flags in two independent reports.","exampleAgentPrompt":"Can you run a numeric distribution analysis and IQR outlier detection on my time series completeness data? I have records for group-a and group-b from 2026-10-01 to 2026-10-04 with observed_points values like 10, 20, 15, 30 and expected_points 1, 2, 3, 4 — analyze the observed_points field across all 4 rows.","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have 150 daily completeness records for my ETL pipeline showing observed_points and expected_points per dataset group — can you run a numeric distribution summary and flag any IQR outliers in the observed_points field so I can spot days where ingestion was abnormal?"},{"title":"Sensor coverage gap detection","prompt":"Here are 80 rows of sensor coverage data by date and sensor group, each with how many readings were observed vs expected. Please analyze the expected_points field for distribution stats and IQR outlier flags so I can identify groups that had unusually high or low targets."},{"title":"Time series quality monitoring report","prompt":"I'm monitoring data quality across two dataset groups over 60 days. Can you give me a numeric distribution report plus IQR outlier flags for the observed_points column in these completeness records so I know which dates to investigate?"}],"resultDescription":"Returns two independent analytical reports for the specified field (observed_points or expected_points): (1) a numeric distribution summary including min, max, mean, median, standard deviation, and quartiles; and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range bounds. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maximum item limit","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Non-numeric or out-of-range values for observed_points or expected_points — rejection","Invalid field enum value (must be 'observed_points' or 'expected_points') — error response","Payment not included or insufficient — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records (observed vs expected data points) and need both a numeric distribution summary and IQR-based outlier detection in a single low-cost call. It is ideal for data quality monitoring workflows where you want statistical characterization and anomaly flagging of completeness metrics without any external data lookup or live prediction. It delivers two reports per call for $0.01 USDC, making it cost-effective for batch completeness audits.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:05.439Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-distribution-iqr-outlier-flags-d9e92875"}