{"uid":"cap_bKE08aWQzI6ppTQp6Lxsh","slug":"time-series-completeness-analyzer-string-normalisation-iqr-outlier-093a1d58","name":"Time Series Completeness Analyzer: String Normalisation + IQR Outlier Flags","description":"Time series completeness — String normalisation + IQR outlier flags: String normalisation + 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-16-032?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_Vua6mcryp5wScP1IfhAG2","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":"Performs string normalisation and IQR-based outlier detection on up to 200 rows of time series completeness records (observed_points vs expected_points), returning two independent analytical reports.","exampleAgentPrompt":"Can you check my time series completeness data for outliers? I have 10 rows from group-a and group-b spanning October 2026 with observed_points ranging from 1 to 50 and expected_points of 10 each — run the IQR outlier flags and string normalisation report on the observed_points field.","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have daily completeness records for two dataset groups over the past 3 months — observed_points and expected_points per day. Can you normalise the strings and flag any IQR outliers in the observed_points field across all 90 rows so I can see which days had anomalous data collection?"},{"title":"Sensor data quality check","prompt":"We have up to 150 rows of sensor reading completeness logs with row IDs, dates from 2025, group labels like 'sensor-north' and 'sensor-south', and observed vs expected point counts. Run the IQR outlier detection on expected_points and give me the two analytical reports."},{"title":"ETL job monitoring anomaly review","prompt":"My ETL monitoring system logged 60 rows of daily completeness records across four pipeline groups — can you run string normalisation and IQR outlier flags on the observed_points field so I can spot which pipeline runs deviated significantly from the norm?"}],"resultDescription":"Returns two independent analytical reports: one covering string normalisation of the input records and one covering IQR-based outlier flags on the selected numeric field (observed_points or expected_points). Each report summarises anomalies and normalised values across up to 200 input rows.","failureModes":["Input exceeds 200 rows — request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","All values identical — IQR is zero, outlier detection may be uninformative","Null values in numeric fields may affect IQR computation"],"whenToPreferThis":"Choose this endpoint when you need a low-cost ($0.01 USDC), self-contained, local analytical engine to simultaneously normalise string fields and detect IQR-based outliers in time series completeness data — no external data sources, live lookups, or ML predictions involved. Ideal for batch data quality checks on up to 200 rows of observed vs expected point counts across grouped time series.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:07.861Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analyzer-string-normalisation-iqr-outlier-093a1d58"}