{"uid":"cap_sDgs7quOD2ho23Mvalnpd","slug":"pipeline-durations-trailing-mean-series-explicit-missing-value-fill-8fe0fd6e","name":"Pipeline Durations — Trailing Mean Series + Explicit Missing-Value Fill","description":"Pipeline durations — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied pipeline durations records (run_seconds, processed_rows). 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-05-154?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","run_seconds","processed_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"run_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","run_seconds":10,"dataset_group":"group-a","processed_rows":1},{"date":"2026-10-02","row_id":"row-2","run_seconds":20,"dataset_group":"group-b","processed_rows":2},{"date":"2026-10-03","row_id":"row-3","run_seconds":15,"dataset_group":"group-a","processed_rows":3},{"date":"2026-10-04","row_id":"row-4","run_seconds":30,"dataset_group":"group-b","processed_rows":4}],"maxItems":200,"minItems":1},"field":{"enum":["run_seconds","processed_rows"],"type":"string","default":"run_seconds"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"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_zQTgsOxpgJq6Z6vgJoMUt","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 fills missing values for pipeline duration records (run_seconds, processed_rows) across up to 200 rows.","exampleAgentPrompt":"Can you compute the trailing mean series (window of 3) for my pipeline run_seconds data and fill any missing values using median imputation? I have about 50 rows with dates and dataset groups I can provide.","exampleUseCases":[{"title":"Smooth ETL job runtime trends","prompt":"I have 120 rows of daily ETL pipeline run times — can you calculate a trailing mean with a window of 7 for run_seconds and fill any gaps using the mean method?"},{"title":"Monitor processed row throughput over time","prompt":"Can you run a trailing mean analysis with a window of 5 on my processed_rows pipeline data, using zero fill for missing values? I'll send you the records with dates and dataset groups."},{"title":"Detect anomalies in pipeline durations","prompt":"I want to smooth out my pipeline run_seconds data with a rolling window of 4 and median-fill any nulls so I can spot anomalies — can you generate that trailing mean series from my 80-row dataset?"}],"resultDescription":"Returns two analytical reports: a trailing mean time series computed over the specified window for the chosen field (run_seconds or processed_rows), and a version of the same series with missing values explicitly filled using the specified method (zero, mean, or median). Results are based entirely on the supplied rows (up to 200) with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds row limit","Invalid date format (not YYYY-MM-DD) — schema validation failure","Window size outside 2–200 range — rejected","Field value not one of run_seconds or processed_rows — enum validation error","Method value not one of zero, mean, median — enum validation error","Payment of 0.01 USDC not provided or rejected — 402 response"],"whenToPreferThis":"Choose this endpoint when you have pipeline duration records (run_seconds or processed_rows) and need both a trailing mean time series and missing-value imputation in a single call for up to 200 rows. It is ideal for offline or batch analytical workflows where no live external data is needed and you want two complementary reports (smoothed series + filled series) at minimal cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:07.814Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-trailing-mean-series-explicit-missing-value-fill-8fe0fd6e"}