{"uid":"cap_o3SP6LPIMVXViMiXTpYFq","slug":"time-series-completeness-analytics-numeric-distribution-interval-filter-69661bce","name":"Time Series Completeness Analytics — Numeric Distribution & Interval Filter","description":"Time series completeness — Numeric distribution + Numeric interval filter: Numeric distribution + Numeric interval filter 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-16-074?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_gOWq90oWQlHcNjFwwFxKP","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 points) by computing numeric distribution statistics and filtering by a numeric interval, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you analyze the distribution of observed_points in my time series completeness data — here are 50 rows with row_id, dataset_group, date, observed_points, and expected_points — and also filter them to only show records where observed_points falls between 0 and 100?","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 120 rows of daily time series completeness records with observed_points and expected_points for two dataset groups — can you run a numeric distribution report on observed_points and also filter to rows where observed_points is between 50 and 500?"},{"title":"Detecting data gaps in sensor feeds","prompt":"Here are my sensor feed completeness records for the last 90 days — row_id, date, group name, observed_points, expected_points. Can you compute the distribution of expected_points and filter it between 10 and 1000 so I can spot anomalies?"},{"title":"QA check on ETL job output completeness","prompt":"I need to QA my ETL jobs — I've got 200 rows of completeness data per day with observed and expected point counts. Run a distribution analysis on observed_points and show me only the records where observed_points is between 0 and 200."}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram buckets) for the selected field (observed_points or expected_points) across all supplied rows, and (2) a filtered subset of rows where the selected field falls within the specified minimum–maximum interval. 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 limit, request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) — schema rejection","Invalid field enum value (not 'observed_points' or 'expected_points') — validation error","Payment of 0.01 USDC not included or declined — 402 payment required response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records (with observed and expected point counts) and need both a numeric distribution summary and an interval-filtered view in a single API call for $0.01 USDC. It is ideal for lightweight, local data-quality analytics without needing an external database, live data connection, or ML prediction service. Prefer alternatives if you need more than 200 rows, real-time data ingestion, predictive analytics, or non-numeric analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:55.076Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analytics-numeric-distribution-interval-filter-69661bce"}