{"uid":"cap_3fNOHa1s7tIqYhaY8EA-I","slug":"time-series-completeness-grouped-numeric-summary-iqr-outlier-flags-945a4e4c","name":"Time Series Completeness — Grouped Numeric Summary + IQR Outlier Flags","description":"Time series completeness — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + 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-16-094?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_pUMYHWCvrywilxRQPAuRD","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 data (observed vs expected points) grouped by dataset category, producing numeric summary statistics and IQR-based outlier flags across up to 200 rows.","exampleAgentPrompt":"I have time series completeness records with observed and expected data points across two groups — can you run a grouped numeric summary and flag IQR outliers on the observed_points field for these rows: group-a on 2026-10-01 had 10 observed / 1 expected, group-b on 2026-10-02 had 20 observed / 2 expected, group-a on 2026-10-03 had 15 observed / 3 expected, and group-b on 2026-10-04 had 30 observed / 4 expected?","exampleUseCases":[{"title":"Pipeline completeness audit by dataset","prompt":"I have 4 months of daily completeness records across two pipelines — can you group them by dataset_group and summarize the observed_points field, then flag any IQR outliers? Here are the rows: group-alpha had 100 observed / 100 expected on 2026-01-01, group-beta had 50 observed / 100 expected on 2026-01-01, and so on for 20 more rows I'll send."},{"title":"Detecting sparse data collection days","prompt":"I'm tracking how many data points my sensors collect each day versus how many I expect. Can you run a grouped numeric summary and IQR outlier detection on the expected_points field for these 15 records across sensor-group-1 and sensor-group-2, so I can see which days had unusually low collection?"},{"title":"QA review of ETL job completeness","prompt":"My ETL jobs produce completeness logs with observed and expected row counts per day per job group. Can you analyze these 30 records, grouped by dataset_group, and give me a numeric summary plus IQR-based outlier flags on observed_points so I can spot which jobs are consistently underperforming?"}],"resultDescription":"Returns two independent analytical reports for the supplied rows: (1) a grouped numeric summary breaking down statistics (mean, median, min, max, etc.) for the selected field (observed_points or expected_points) by dataset_group, and (2) IQR-based outlier flags identifying which rows contain values that fall outside the interquartile range thresholds within their group. No external data is fetched; all analysis is performed on the submitted rows only.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","field enum value not one of 'observed_points' or 'expected_points' — invalid input","Payment not provided or insufficient USDC — x402 payment required response","Groups with too few rows for IQR computation may yield sparse or undefined outlier results"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness records (observed vs expected data points) and need grouped statistical summaries plus IQR-based outlier detection in a single call, without requiring any external data lookup or live monitoring. Ideal for batch QA pipelines, ETL audits, or data coverage checks where data is already assembled and you need fast local analytical output for up to 200 rows across multiple groups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:03.331Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-grouped-numeric-summary-iqr-outlier-flags-945a4e4c"}