{"uid":"cap_gkw-gbl20I1V4ZjvWG9c3","slug":"time-series-completeness-adjacent-row-changes-iqr-outlier-flags-bb098a95","name":"Time Series Completeness: Adjacent Row Changes + IQR Outlier Flags","description":"Time series completeness — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags 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-16-157?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"}}},"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_oCRUVIUAKguGycPP57VDO","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 (observed vs expected data points) for adjacent row-to-row changes and IQR-based outlier detection, returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run adjacent row change and IQR outlier detection on my completeness records? I have 30 daily rows with observed_points and expected_points for two dataset groups — flag anything suspicious in the observed_points field.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 60 days of completeness logs for my data pipeline — each row has a date, observed_points, and expected_points. Can you check them for sudden adjacent-row jumps and IQR outliers in the observed_points field so I can see which days had anomalous collection rates?"},{"title":"Multi-group sensor data gap detection","prompt":"My IoT sensor network has two groups reporting daily. I want to analyze 90 rows of completeness data — row IDs, dates, dataset groups, and both observed and expected point counts — looking for IQR outliers in expected_points across the whole series."},{"title":"Data quality SLA monitoring","prompt":"We track daily completeness for our SLA reporting. Can you take these 45 rows with observed_points and expected_points across three dataset groups and flag both adjacent-row swings and statistical outliers in observed_points so I know where we missed targets?"}],"resultDescription":"Returns two independent analytical reports for the supplied rows: (1) adjacent row change analysis showing period-over-period deltas in the selected field (observed_points or expected_points), and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range bounds. No external lookups or live data — analysis is performed purely on the submitted rows.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Null values in numeric fields may affect IQR computation accuracy","Payment of 0.01 USDC not completed — 402 payment required response","Invalid field selector (not 'observed_points' or 'expected_points') — enum validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of time series completeness data (observed vs expected point counts) and need both adjacent row change detection and IQR-based outlier flagging in a single low-cost call. It is ideal for data quality monitoring pipelines, SLA audits, or anomaly triage where no external data or predictions are needed — just statistical analysis on your submitted records. Prefer this over general analytics tools when you need two complementary reports (trend changes + statistical outliers) on completeness metrics specifically.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:45:47.073Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-adjacent-row-changes-iqr-outlier-flags-bb098a95"}