{"uid":"cap_hYEeSe4LYHZmzLXKyd6pG","slug":"time-series-completeness-numeric-interval-filter-iqr-outlier-flags-2dbb057b","name":"Time Series Completeness: Numeric Interval Filter + IQR Outlier Flags","description":"Time series completeness — Numeric interval filter + IQR outlier flags: Numeric interval filter + 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-16-130?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_zRhn-cGEqVkJENQ6fkJqN","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 filtering rows within a numeric interval and flagging IQR-based outliers across up to 200 rows in two independent reports.","exampleAgentPrompt":"I have daily completeness records for two dataset groups from October 2026 — each row has observed_points and expected_points. Can you filter the observed_points field to only rows between 0 and 100, flag any IQR outliers, and give me two analytical reports across all 4 rows?","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I'm monitoring two data pipelines daily and I have 90 days of records with observed_points and expected_points per group. Filter observed_points between 0 and 500, flag any IQR outliers, and give me the two-report analysis so I can see which days had abnormal completeness."},{"title":"Sensor reading gap detection","prompt":"I've got 150 rows of sensor time series data with observed and expected measurement counts per day per sensor group. Run the numeric interval filter on observed_points from 10 to 1000 and flag any IQR-based outliers so I can identify which sensors had unusual gaps."},{"title":"ETL job monitoring anomaly check","prompt":"Our ETL jobs log expected vs observed record counts each day across several groups. I have about 80 rows covering the last quarter — can you filter expected_points between 50 and 5000 and flag IQR outliers to spot which jobs were statistically anomalous?"}],"resultDescription":"Returns two independent analytical reports: one covering rows that fall within the specified numeric interval for the chosen field (observed_points or expected_points), and one flagging rows that are IQR-based statistical outliers. Each report operates on the same supplied dataset of up to 200 time series completeness rows without any external data lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date field not in YYYY-MM-DD format — schema pattern mismatch","Minimum value greater than maximum value — logically invalid interval producing empty filter results","Null numeric values in the target field — rows may be excluded from interval filtering"],"whenToPreferThis":"Choose this endpoint when you have structured time series completeness data (observed vs expected point counts) and need both numeric range filtering and IQR-based outlier detection in a single call, without requiring any external data sources, predictions, or live lookups. It is ideal for data quality monitoring workflows where you want two complementary statistical views — interval membership and distributional anomalies — on the same dataset for up to 200 rows at minimal cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:18.782Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-interval-filter-iqr-outlier-flags-2dbb057b"}