{"uid":"cap_aCyZLKkJfVmnMGZZYY8Q2","slug":"time-series-completeness-numeric-interval-filter-two-measure-e66c198d","name":"Time Series Completeness — Numeric Interval Filter + Two-Measure Correlation","description":"Time series completeness — Numeric interval filter + Two-measure correlation: Numeric interval filter + Two-measure correlation 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-16-131?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_jpyf5Lcg8yim5TPFq6xYm","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":"Filters time series completeness records by a numeric interval and computes correlation between observed_points and expected_points across up to 200 rows, producing two analytical reports.","exampleAgentPrompt":"I have daily completeness records for two dataset groups from October 1–4, 2026, each with observed_points and expected_points values — can you filter to rows where observed_points falls between 10 and 30, then run a two-measure correlation report between observed_points and expected_points across all 200 rows?","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have 150 rows of daily pipeline completeness records for our ingestion jobs, each with an observed_points and expected_points count. Filter to rows where observed_points is between 5 and 500, then give me a two-measure correlation report showing how closely observed tracks expected over time."},{"title":"Sensor coverage gap analysis","prompt":"Here are 200 rows of sensor coverage data across two sensor groups from the past year — each row has a date, observed_points, and expected_points. Filter on expected_points from 0 to 1000 and run the correlation analysis between observed and expected so I can see where coverage gaps correlate with expectations."},{"title":"Monitoring dataset quality check","prompt":"I've got completeness records for three monitoring dataset groups, about 80 rows total, with observed and expected point counts per day. Can you filter to observed_points between 20 and 200 and produce the two-report correlation analysis so I can assess data quality across groups?"}],"resultDescription":"Returns two independent analytical reports: (1) rows from the supplied dataset filtered to the specified numeric interval on the chosen field, and (2) a correlation analysis between observed_points and expected_points across those records, all computed locally with no external lookups.","failureModes":["Fewer than 1 or more than 200 rows supplied — request rejected","Date values not in YYYY-MM-DD format — validation error","observed_points or expected_points outside allowed numeric range — schema error","minimum greater than maximum — logically invalid interval filter","Insufficient variance in data to compute meaningful correlation — degenerate result","Missing required fields (row_id, date, observed_points, expected_points) on any row — rejection"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness records (observed vs expected point counts) and need both a numeric range filter and a correlation analysis between the two measures in a single $0.01 call, without requiring live data lookup, prediction, or external data enrichment. Ideal for offline data quality audits, pipeline monitoring reviews, and batch completeness assessments on up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:54.765Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-interval-filter-two-measure-e66c198d"}