{"uid":"cap_ljtDu4dAq9PqrRv_osquN","slug":"time-series-completeness-numeric-row-ordering-secondary-measure-c3b065c6","name":"Time Series Completeness — Numeric Row Ordering + Secondary-Measure Weighted Mean","description":"Time series completeness — Numeric row ordering + Secondary-measure weighted mean: Numeric row ordering + Secondary-measure weighted mean 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-16-121?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_2gDyV6iVYuQrEwoaakVwU","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":"Computes numeric row ordering and secondary-measure weighted mean analytics on time series completeness records (observed_points vs expected_points) for up to 200 rows, returning two independent reports.","exampleAgentPrompt":"Analyze this time series completeness data for up to 200 rows — I need both a numeric row ordering report and a secondary-measure weighted mean on the observed_points field across my dataset groups and dates.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have a list of daily ETL runs with observed_points and expected_points for each dataset group from last month — can you rank the rows numerically and compute the weighted mean on observed_points so I can see where coverage is lagging?"},{"title":"Sensor data gap detection","prompt":"Here are 150 rows of sensor readings with observed and expected data points per day across several device groups. Run the row ordering and weighted mean analysis on expected_points so I can identify which days have the worst relative gaps."},{"title":"Multi-group completeness benchmarking","prompt":"I want to compare completeness across my dataset groups — I have 80 rows with dates, observed_points, and expected_points. Give me both the numeric row ordering and a secondary-measure weighted mean on observed_points so I can benchmark each group."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric row ordering of the submitted completeness records, and (2) a secondary-measure weighted mean computed over the specified field (observed_points or expected_points), both derived from the supplied rows without any external lookup or prediction.","failureModes":["Exceeds 200-row limit — request rejected or truncated","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Null values in observed_points or expected_points may affect weighted mean accuracy","Invalid field enum value (not 'observed_points' or 'expected_points') — rejected","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous statistical reports — numeric row ordering plus weighted mean — on time series completeness records (observed vs expected data points) for up to 200 rows, all computed locally without external data lookups. Prefer this over single-report analytics endpoints when both ranking and weighted aggregation are needed in a single $0.01 call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:22.528Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-row-ordering-secondary-measure-c3b065c6"}