{"uid":"cap_gYQrRO5q7hV7cjNm0ofm1","slug":"time-series-completeness-numeric-distribution-daily-sum-series-8bb08855","name":"Time Series Completeness — Numeric Distribution + Daily Sum Series","description":"Time series completeness — Numeric distribution + Daily sum series: Numeric distribution + Daily sum series 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-16-075?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_Nk_6upM2Mgtt3pKi2bFEH","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 a numeric distribution report and a daily sum series report for up to 200 rows.","exampleAgentPrompt":"I have 50 rows of daily time series completeness records for two dataset groups — each row has an observed_points and expected_points count for dates in October 2026. Can you run a numeric distribution and daily sum series analysis on the observed_points field for me?","exampleUseCases":[{"title":"Data pipeline gap detection","prompt":"I've got completeness records for my ETL pipeline across groups 'ingest' and 'transform' from January through March 2026 — each row has how many data points we observed vs expected each day. Run a numeric distribution and daily sum series on the observed_points so I can spot where we had the biggest gaps."},{"title":"Multi-group completeness audit","prompt":"I have 120 rows of daily completeness data for four dataset groups from Q3 2025, with observed_points and expected_points for each date. Can you produce a numeric distribution and daily sum series report on the expected_points field so I can compare targets across groups?"},{"title":"Sensor data coverage summary","prompt":"Here are 80 rows of sensor telemetry completeness records — each row has a date, a sensor group, and observed vs expected readings. Please analyze the observed_points field and give me a numeric distribution plus a daily sum series showing how coverage varied day by day."}],"resultDescription":"Returns two analytical reports sharing the same input data: (1) a numeric distribution of the selected field (observed_points or expected_points) across all supplied rows, and (2) a daily sum series aggregating the selected field by date — up to 200 rows processed per call.","failureModes":["Missing required fields (row_id, date, observed_points, expected_points) causes validation failure","Date values not matching YYYY-MM-DD pattern will be rejected","Submitting more than 200 rows exceeds the maxItems limit and will be rejected","Null values in the selected numeric field may affect distribution calculations","Empty rows array (minItems: 1) will return an error","Invalid field enum value (must be 'observed_points' or 'expected_points') causes rejection"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness data (observed vs expected data points by date and group) and need both a numeric distribution and a daily sum series in one call for up to 200 rows. It is a local analytical compute service — ideal when you have the data already and just need fast statistical summaries without any external lookups, predictions, or live data fetching.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:55.862Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-distribution-daily-sum-series-8bb08855"}