{"uid":"cap_6oF0BDEYDtAfjRTt8gEUJ","slug":"time-series-completeness-grouped-numeric-summary-adjacent-row-changes-549c2d97","name":"Time Series Completeness — Grouped Numeric Summary + Adjacent Row Changes","description":"Time series completeness — Grouped numeric summary + Adjacent row changes: Grouped numeric summary + 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-16-091?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__F3rcUPmFNDjiaHly8x5_","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) to produce grouped numeric summaries and adjacent row change reports for up to 200 rows.","exampleAgentPrompt":"Can you run a grouped numeric summary and adjacent row change report on my time series completeness data? I have rows with dataset_group, date, observed_points, and expected_points — here are up to 200 rows — and I want to analyze the observed_points field.","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have daily completeness records for two data pipelines — 'pipeline-alpha' and 'pipeline-beta' — from the past 30 days, each with observed and expected data points. Can you group them and give me a numeric summary plus show me how the observed_points changed between consecutive days?"},{"title":"Detect drops in sensor data coverage","prompt":"I'm tracking sensor readings completeness across three sensor groups over the last 60 days. Can you analyze the observed_points vs expected_points records, group them by sensor group, and tell me where the biggest row-to-row drops are?"},{"title":"Weekly completeness trend summary by dataset","prompt":"I have 150 rows of weekly time series completeness data across multiple dataset groups — each row has a date, group name, observed_points, and expected_points. Can you summarize the expected_points statistics per group and also show adjacent row changes for expected_points?"}],"resultDescription":"Two analytical reports based on the supplied rows: (1) a grouped numeric summary showing aggregate statistics (e.g. count, min, max, mean) of the selected field broken down by dataset_group; and (2) an adjacent row changes report showing the difference in the selected field between consecutive rows. Both reports are computed locally with no external lookups.","failureModes":["Missing required fields (row_id, dataset_group, date, observed_points, expected_points) will cause validation errors","Exceeding 200 rows returns an error or truncation","Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation","Null values in the selected field may affect numeric summary calculations","Empty rows array is rejected (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness records and need two complementary statistical views — grouped summaries and sequential row differences — in a single inexpensive call. It is ideal for data quality monitoring, pipeline auditing, or completeness dashboards where you supply the data and want fast, offline analytical computation without any external data fetch or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:04.546Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-grouped-numeric-summary-adjacent-row-changes-549c2d97"}