{"uid":"cap_7KDPb7Yo1wxcqKGjQykSm","slug":"time-series-completeness-daily-sum-series-iqr-outlier-flags-f9193af4","name":"Time Series Completeness — Daily Sum Series + IQR Outlier Flags","description":"Time series completeness — Daily sum series + IQR outlier flags: Daily sum series + 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-16-140?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_Acd3mx-XDGZI-8rYblhFL","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":"Computes daily aggregated sums and flags statistical outliers using IQR analysis for up to 200 time series completeness records containing observed and expected data points.","exampleAgentPrompt":"Analyze my time series completeness data — here are 200 rows with row_id, dataset_group, date, observed_points, and expected_points fields — compute the daily sum series and flag any IQR outliers on the observed_points field.","exampleUseCases":[{"title":"Pipeline data gap detection","prompt":"I have 150 rows of daily completeness logs for my ETL pipeline, each with observed and expected point counts per dataset group. Can you compute the daily sums and flag any days where the observed_points look like IQR outliers so I can investigate data gaps?"},{"title":"IoT sensor coverage audit","prompt":"Here are completeness records for my IoT sensor network over the past six months — about 180 rows with dates, observed_points, and expected_points grouped by sensor cluster. Run the daily sum series and IQR outlier detection on expected_points so I can see which days had unusual coverage targets."},{"title":"Data warehouse ingestion QA","prompt":"I'm doing a QA pass on our data warehouse ingestion — I've got 90 rows of completeness records by dataset_group and date with observed and expected counts. Can you give me the daily aggregated totals and highlight any outlier days using IQR on the observed_points?"}],"resultDescription":"Returns two analytical reports sharing local computation engines: (1) a daily sum series aggregating observed_points or expected_points by date across dataset groups, and (2) IQR-based outlier flags identifying which rows or dates fall outside the interquartile range. No external lookups or predictions are made; all analysis is performed on the supplied data up to 200 rows.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Insufficient data points for meaningful IQR calculation (very few rows) — degenerate statistical output","null values in observed_points or expected_points may affect aggregation accuracy"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness records (observed vs expected data points) and need both a daily aggregated sum view and statistical outlier detection in a single low-cost call. It is ideal for data quality pipelines, ETL monitoring, and sensor coverage audits where you supply the data directly and need offline statistical analysis rather than live lookups or predictions. At $0.01 USDC for two reports covering up to 200 rows, it is cost-effective for batch completeness QA tasks.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:08.853Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-daily-sum-series-iqr-outlier-flags-f9193af4"}