{"uid":"cap_qEfXz4g1M4DLSqcfn0RVC","slug":"time-series-completeness-base-100-series-explicit-missing-value-fill-ebf31b8d","name":"Time Series Completeness — Base-100 Series + Explicit Missing-Value Fill","description":"Time series completeness — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill 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-16-169?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_xwHYQLdTjuhqrU7n_7k9O","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 a base-100 indexed series and fills missing values (zero, mean, or median) for time series completeness data containing observed vs. expected point counts.","exampleAgentPrompt":"I have daily completeness records for two dataset groups — group-a and group-b — from October 2026, with observed_points and expected_points for each row. Can you run a base-100 indexed series starting from row index 0 and fill any missing observed_points values using median imputation, for up to 4 rows?","exampleUseCases":[{"title":"Monitoring pipeline data completeness over time","prompt":"I'm tracking whether our data pipelines are collecting the right number of points each day. I have 30 rows of daily records with observed_points and expected_points for two pipelines, group-alpha and group-beta. Can you index the observed_points to base-100 starting at row 0 and fill any missing values using the mean, so I can see relative completeness trends?"},{"title":"Normalizing sensor data for cross-site comparison","prompt":"I have sensor completeness records from two sites across 60 days — each row has a date, site name, observed_points, and expected_points. Please produce a base-100 series for expected_points anchored at row index 0 and fill any gaps with zero so I can compare both sites on a level playing field."},{"title":"Auditing historical data gaps in a batch import","prompt":"We're auditing a historical import of 120 daily records across dataset groups 'ingest-v1' and 'ingest-v2'. Can you generate a base-100 indexed series starting at row 0 and use median fill for any missing observed_points values, so we can see where collection fell short relative to expectations?"}],"resultDescription":"Two analytical reports: (1) a base-100 indexed series where values are normalized relative to the specified base row, and (2) an explicit missing-value filled series using the chosen imputation method (zero, mean, or median) — both applied to the selected field (observed_points or expected_points) across up to 200 input rows with row_id, dataset_group, date, observed_points, and expected_points.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","base_index out of range (0–199) causes an error","null values in both observed_points and expected_points may produce degenerate fill results","Payment not included or insufficient USDC — x402 payment required before response is returned"],"whenToPreferThis":"Choose this endpoint when you need both a base-100 normalized time series view AND missing-value imputation on completeness data (observed vs. expected point counts) in a single $0.01 call covering up to 200 rows. It is ideal for data quality monitoring, pipeline audits, and cross-group completeness comparisons where you want relative indexing and gap filling without any external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:23.987Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-base-100-series-explicit-missing-value-fill-ebf31b8d"}