{"uid":"cap_I_LV9XXJsYuJvgmHbjT8D","slug":"time-series-completeness-numeric-row-ordering-iqr-outlier-flags-112223bc","name":"Time Series Completeness — Numeric Row Ordering + IQR Outlier Flags","description":"Time series completeness — Numeric row ordering + IQR outlier flags: Numeric row ordering + 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-16-119?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_aApQOLhADu-SJ5KBB8S77","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 up to 200 time series completeness records (observed_points vs expected_points) to produce numeric row ordering and IQR-based outlier flags across two independent reports.","exampleAgentPrompt":"Can you analyze my time series completeness data for observed_points across these 5 rows — row-1 through row-5 for group-alpha from 2025-01-01 to 2025-01-05, with expected_points of 100 each day and observed_points of 95, 102, 88, 200, 99 — and flag any IQR outliers while ranking the rows numerically?","exampleUseCases":[{"title":"Detecting completeness spikes in data pipelines","prompt":"I have daily pipeline completeness logs for two dataset groups from the past month — can you run IQR outlier detection on the observed_points field across all 30 rows to find which dates had abnormally high or low data collection?"},{"title":"Ranking sensor data completeness records","prompt":"Here are 50 rows of sensor completeness records with observed_points and expected_points per date — can you sort them numerically by observed_points and flag which rows are statistical outliers using IQR?"},{"title":"Validating ETL job completeness anomalies","prompt":"My ETL jobs ran daily for 60 days across three dataset groups — can you analyze the expected_points vs observed_points completeness data, rank all rows, and flag any IQR outliers so I know which days had suspicious drops or spikes?"}],"resultDescription":"Returns two independent analytical reports per call: (1) numeric ordering of the submitted rows by the selected field (observed_points or expected_points), and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. No external lookups or predictions are made — all analysis is performed on the supplied data only.","failureModes":["More than 200 rows submitted — input exceeds maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Null values in both observed_points and expected_points may reduce IQR reliability","Invalid field enum value — must be 'observed_points' or 'expected_points'","Payment not included or insufficient — x402 payment required at $0.01 USDC"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records (observed vs expected data points) and need both numeric row ranking and IQR outlier flagging in a single call. It is ideal for data quality monitoring pipelines, ETL validation, and sensor completeness auditing where no external lookups or predictive modeling are needed — just fast, local statistical analysis on your supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:00.563Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-row-ordering-iqr-outlier-flags-112223bc"}