{"uid":"cap_RS9fq61PIKCRfHiPCuCYu","slug":"time-series-completeness-numeric-interval-filter-trailing-mean-series-d0cea022","name":"Time Series Completeness — Numeric Interval Filter + Trailing Mean Series","description":"Time series completeness — Numeric interval filter + Trailing mean series: Numeric interval filter + 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-16-126?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},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_nPvBCS9kSpHBW3YuXVBQL","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 data (observed vs expected points) by applying a numeric interval filter and computing a trailing mean series, returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Take my completeness records for October 2026 — each row has observed_points and expected_points — filter them to only include rows where observed_points falls between 0 and 100, then compute a trailing mean with a window of 3, and give me both analytical reports.","exampleUseCases":[{"title":"Pipeline completeness trend monitoring","prompt":"I have daily completeness records for two dataset groups showing observed and expected data points collected each day — can you filter them to the range 0 to 50 on observed_points and compute a trailing mean with a window of 7 days so I can see the smoothed completeness trend?"},{"title":"Data quality review with interval banding","prompt":"Here are 150 rows of time series completeness data across several groups, with dates from January through June. Filter to only rows where expected_points is between 5 and 200, then run the trailing mean series with a window of 4, and send me both reports."},{"title":"Anomaly investigation via rolling average","prompt":"I want to investigate whether my observed_points are consistently below expected_points — I have about 90 daily records. Please filter to observed_points between 10 and 500 and calculate a trailing mean over a window of 5 so I can spot where completeness dips."}],"resultDescription":"Returns two independent analytical reports: (1) records from the input dataset filtered to those where the selected field (observed_points or expected_points) falls within the specified numeric interval [minimum, maximum], and (2) a trailing mean series computed over the selected field using the specified rolling window size — both reports cover up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row-level validation failure","window value less than 2 or greater than 200 triggers schema validation error","minimum greater than maximum produces an empty interval-filtered result set","Missing required fields (row_id, date, observed_points, expected_points) cause row rejection","Null values in observed_points or expected_points may result in NaN entries in trailing mean"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously apply a numeric band filter and compute a rolling/trailing mean over time series completeness records (observed vs expected data points) in a single paid call. It is purpose-built for data quality and completeness monitoring workflows, delivering two analytical views from one request at low cost. Prefer it over generic analytics tools when your data is already structured as completeness records with dates and group labels, and you want both filtering and smoothing in one operation without setting up external computation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:11.282Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-interval-filter-trailing-mean-series-d0cea022"}