{"uid":"cap_OR7hNnfbd_TJhxbF7iGe2","slug":"transformation-throughput-trailing-mean-series-secondary-measure-485c5cd0","name":"Transformation Throughput — Trailing Mean Series + Secondary-Measure Weighted Mean","description":"Transformation throughput — Trailing mean series + Secondary-measure weighted mean: Trailing mean series + Secondary-measure weighted mean for supplied transformation throughput records (processed_rows, duration_seconds). 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-13-151?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"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},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"},"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_ybwBAvqY4eDdRSSacfvLa","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 secondary-measure weighted mean for transformation throughput records (processed_rows, duration_seconds) over up to 200 rows.","exampleAgentPrompt":"Calculate the trailing mean series and secondary-measure weighted mean for my transformation throughput records — I have 50 rows with processed_rows and duration_seconds per date and dataset_group, using a window of 7 and focusing on the processed_rows field.","exampleUseCases":[{"title":"ETL pipeline throughput trend analysis","prompt":"I have daily ETL job records with processed_rows and duration_seconds for two dataset groups over the past 3 months — can you compute the trailing mean series with a window of 14 and also get the weighted mean of processed_rows weighted by duration_seconds?"},{"title":"Data warehouse load performance review","prompt":"Here are 120 rows of my data warehouse load jobs with dates, dataset groups, processed rows, and duration seconds — please generate a trailing mean series using a window of 5 for processed_rows and also the weighted mean report."},{"title":"Comparing batch processing efficiency across groups","prompt":"I want to understand how my batch processing throughput has trended — I have 80 records with processed_rows and duration_seconds across multiple dataset_groups. Run a trailing mean with window size 10 on duration_seconds and give me the secondary-measure weighted mean too."}],"resultDescription":"Returns two independent analytical reports: (1) a trailing mean time-series for the specified field (processed_rows or duration_seconds) computed over the given sliding window, and (2) a secondary-measure weighted mean where one measure is weighted by the other — both derived entirely from the supplied row data without any external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, date, processed_rows, duration_seconds) causes validation error","Window size larger than the number of input rows may produce incomplete or empty trailing series","Null values in processed_rows or duration_seconds may affect weighted mean calculation","Invalid date format (not YYYY-MM-DD) causes schema validation failure"],"whenToPreferThis":"Choose this endpoint when you need both a rolling/trailing mean time-series AND a weighted mean statistic for transformation throughput data (processed_rows and duration_seconds) in a single low-cost call. It is ideal for offline analytical batch summaries of ETL or data pipeline performance records where no external data, predictions, or live lookups are needed — and when your dataset fits within 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:11.948Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-trailing-mean-series-secondary-measure-485c5cd0"}