{"uid":"cap_7UXt7WAPKqbrApUchg1Hf","slug":"feature-availability-analytics-trailing-mean-series-two-measure-58f46188","name":"Feature Availability Analytics: Trailing Mean Series + Two-Measure Correlation","description":"Feature availability — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied feature availability records (populated_features, expected_features). 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-10-150?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"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_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"},"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_AKZcnplCfk31yS6IBq0wR","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 trailing mean time series and a two-measure correlation report for supplied feature availability data (populated_features vs expected_features) over up to 200 rows.","exampleAgentPrompt":"Run a trailing mean series and correlation analysis on my feature availability data — I have 50 rows with date, dataset_group, populated_features, and expected_features columns — use a window of 7 and analyze the populated_features field.","exampleUseCases":[{"title":"ML pipeline feature completeness monitoring","prompt":"I have daily records of how many features were populated vs expected in my ML pipeline — can you run a trailing mean with a window of 14 and a correlation between populated and expected features across these 120 rows to see if my data completeness is trending up or down?"},{"title":"Dataset group coverage comparison","prompt":"For each of my two dataset groups I have 90 rows of populated_features and expected_features by date — compute a rolling mean over a window of 5 and correlate the two measures so I can see which group has better feature coverage."},{"title":"Feature store quality audit","prompt":"I want to audit my feature store's fill rate — I have 200 date-stamped rows with populated_features and expected_features values. Can you calculate the trailing mean series using a window of 10 and also give me the correlation between the two measures?"}],"resultDescription":"Returns two analytical reports sharing local computation engines: (1) a trailing mean time series for the selected measure (populated_features or expected_features) over the specified window, and (2) a correlation report between populated_features and expected_features across the supplied rows. No external data is fetched; all analysis is performed on the submitted rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Window size larger than the number of rows — trailing mean may be undefined or partially computed","Missing required fields (row_id, date, populated_features, expected_features) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Null values in measure columns may reduce correlation sample size or produce NaN results"],"whenToPreferThis":"Choose this endpoint when you have tabular feature availability records with date, group, and two numeric measures (populated vs expected features) and need both a rolling average trend and a correlation between the two measures in a single $0.01 call. It is ideal for lightweight, offline analytics on up to 200 rows without needing a full data warehouse or external ML service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T06:47:13.615Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-trailing-mean-series-two-measure-58f46188"}