{"uid":"cap_TDncSzzfscPplEwIDTwcs","slug":"time-series-completeness-numeric-row-ordering-adjacent-row-changes-b1fa12b1","name":"Time Series Completeness — Numeric Row Ordering & Adjacent Row Changes","description":"Time series completeness — Numeric row ordering + Adjacent row changes: Numeric row ordering + Adjacent row changes 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-16-116?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_f7DsiduZrznWcfXyM4oKI","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 data (observed vs expected points) to produce two reports: numeric row ordering and adjacent row changes, for up to 200 rows.","exampleAgentPrompt":"Can you check my time series completeness data for anomalies? I have up to 200 rows with dates, dataset groups, and observed vs expected point counts — I want both a numeric ordering report and an adjacent row change report based on the observed_points field.","exampleUseCases":[{"title":"Daily data pipeline completeness audit","prompt":"I have 30 days of pipeline logs with observed and expected event counts per day per dataset group. Can you run a completeness analysis on them to see the row ordering and how much the observed_points changed from day to day?"},{"title":"Detecting data collection gaps in sensor feeds","prompt":"My IoT sensors are supposed to record a fixed number of readings daily but sometimes miss data. I have 90 rows of dates with observed and expected point counts — can you give me a numeric ordering and adjacent row change breakdown so I can spot the gaps?"},{"title":"QA report for a data warehouse ingestion job","prompt":"We're doing QA on our warehouse ingestion — I have a table of up to 150 rows with row_id, dataset_group, date, observed_points, and expected_points for expected_points field. Can you produce the numeric ordering and adjacent row changes reports to flag inconsistencies?"}],"resultDescription":"Two analytical reports computed locally: (1) a numeric row ordering of the supplied time series rows ranked by the selected field (observed_points or expected_points), and (2) an adjacent row changes report showing the delta between consecutive rows — all based solely on the supplied data with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Dates not matching YYYY-MM-DD format fail pattern validation","Null values in the selected field may produce incomplete change calculations","Invalid field enum value (not 'observed_points' or 'expected_points') causes a schema error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, deterministic completeness diagnostics on a small time series dataset (up to 200 rows) without requiring external data sources, live predictions, or professional assessment. It is ideal for quick QA passes on ingestion pipelines, sensor feeds, or any scenario where you want both a ranked ordering and a day-over-day delta report from the same call at minimal cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:02.329Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-row-ordering-adjacent-row-changes-b1fa12b1"}