{"uid":"cap_UnNsOXGSNNqgsC0i5w3cF","slug":"time-series-completeness-numeric-distribution-trailing-mean-series-2f9af90d","name":"Time Series Completeness — Numeric Distribution + Trailing Mean Series","description":"Time series completeness — Numeric distribution + Trailing mean series: Numeric distribution + Trailing mean 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-16-076?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"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_7AbkiS8WEK2SYkZGM47hU","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 a numeric distribution report and a trailing mean series for time series completeness records (observed_points vs expected_points) across up to 200 rows.","exampleAgentPrompt":"Can you run a numeric distribution and trailing mean series analysis on my time series completeness data? I have observed_points and expected_points for each day, grouped into dataset groups, and I want to analyze the observed_points field with a rolling window of 7.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have daily records for my data pipeline showing how many data points were observed versus expected for each dataset group from October 1 to October 30. Can you run a numeric distribution and a trailing mean series on the observed_points field with a window of 5 to help me understand where completeness is dropping?"},{"title":"Multi-group dataset completeness review","prompt":"We track data completeness across three dataset groups every day — can you analyze the expected_points field with a trailing window of 10 days and give me the numeric distribution and rolling mean so I can spot which groups are consistently underdelivering?"},{"title":"Completeness trend monitoring for SLA reporting","prompt":"I need to report on our data completeness SLA this month. I have up to 150 daily rows with observed and expected point counts. Can you compute the numeric distribution and the trailing mean series on observed_points using a window of 7 so I can show the trend to stakeholders?"}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution summarizing the spread and statistics of the selected field (observed_points or expected_points) across all supplied rows, and (2) a trailing mean series showing the rolling average of that field over the specified window size, both computed locally from the supplied data without any external lookup or prediction.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Window size outside range 2–200 — rejected","Null values in numeric fields may affect distribution and trailing mean calculations"],"whenToPreferThis":"Use this endpoint when you need both a numeric distribution and a trailing mean series for time series completeness data (observed vs expected points) in a single call at low cost. It is ideal for batch auditing up to 200 rows of completeness records by date and dataset group, particularly when you want two statistical views in one request without writing custom analytics code.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:13.124Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-distribution-trailing-mean-series-2f9af90d"}