{"uid":"cap_jJ-u9s72ssFUh3TPbeKOF","slug":"time-series-completeness-numeric-interval-filter-adjacent-row-changes-52d7c081","name":"Time Series Completeness — Numeric Interval Filter + Adjacent Row Changes","description":"Time series completeness — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes 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-16-127?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"},"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_yf8JOj0Yz43nThYWO08KG","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 records by applying a numeric interval filter and detecting adjacent row changes across observed_points and expected_points fields, returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 50 daily time series completeness records with observed_points and expected_points — can you filter rows where observed_points falls between 0 and 100, and also detect adjacent row changes in the same dataset? The rows cover groups like 'group-a' and 'group-b' and are dated from 2026-10-01 onward.","exampleUseCases":[{"title":"Daily completeness gap detection","prompt":"I have 120 rows of daily time series completeness records across two dataset groups. Can you filter rows where expected_points falls between 5 and 500, and also flag any adjacent rows that show a big change in the expected_points values?"},{"title":"Audit observed data point spikes","prompt":"Run an interval filter on my 80-row completeness dataset — I want to see which rows have observed_points between 10 and 250 — and also give me a report on adjacent row changes in observed_points so I can spot sudden spikes or drops."},{"title":"Multi-group time series QA","prompt":"I've got completeness records for several dataset groups going back 6 months, about 175 rows total. Can you apply a numeric filter to keep only rows where observed_points is between 0 and 1000, and at the same time tell me where consecutive rows change significantly?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric interval filter report identifying which rows have the selected field (observed_points or expected_points) within the specified minimum/maximum bounds, and (2) an adjacent row changes report showing differences between consecutive rows for the same field, for up to 200 input rows.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Invalid date format (must match YYYY-MM-DD pattern)","Minimum value greater than maximum value resulting in an empty interval","Null values in observed_points or expected_points may affect change detection calculations"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously (1) filter time series completeness records by a numeric range on observed or expected points, and (2) detect value changes between adjacent rows — all in a single paid call at $0.01 USDC for up to 200 rows. Prefer this over single-report endpoints when both interval filtering and row-to-row change analysis are needed together without external lookups or live data sources.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:29.640Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-interval-filter-adjacent-row-changes-52d7c081"}