{"uid":"cap_1IywFWECxI8SwGfcE2iSd","slug":"time-series-completeness-numeric-interval-filter-keyed-dataset-d99b1de5","name":"Time Series Completeness — Numeric Interval Filter + Keyed Dataset Comparison","description":"Time series completeness — Numeric interval filter + Keyed dataset comparison: Numeric interval filter + Keyed dataset comparison 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-16-134?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}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"},"other":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","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":21},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":17},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":33}],"maxItems":200,"minItems":0},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_yhdtJAx69VtudngLNYdx2","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 records by applying numeric interval filtering and comparing two keyed datasets (observed vs expected data points) across up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Can you check the completeness of my sensor data by comparing two dataset versions — I'll give you up to 50 rows with observed_points and expected_points per date grouped as 'group-a' and 'group-b', filter on the observed_points field, and compare both datasets to see where they diverge?","exampleUseCases":[{"title":"IoT sensor data gap detection","prompt":"I have daily sensor readings for two device groups over the past month — each row has a date, group name, how many data points we observed, and how many we expected. Can you filter by observed_points intervals and compare the baseline dataset against this week's dataset to find where our sensors are falling short?"},{"title":"ETL pipeline completeness audit","prompt":"Our ETL pipeline produces daily record counts per data group. I have two runs of the same pipeline with observed and expected row counts — can you run an interval filter on observed_points and do a keyed comparison between the two runs to spot any completeness regressions? I have about 120 rows."},{"title":"A/B data collection completeness check","prompt":"We're running two parallel data collection experiments, each with their own observed and expected point counts by date and group. Can you compare these two datasets against each other using the keyed comparison and also filter the expected_points by numeric interval to identify which groups are underperforming?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric interval filter report showing which rows fall within specified numeric ranges for the selected field (observed_points or expected_points), and (2) a keyed dataset comparison report highlighting differences between the two supplied time series datasets, keyed by row_id, dataset_group, and date. No external lookups or predictions are made — all analysis is performed locally on the supplied data.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors","Date values not matching YYYY-MM-DD pattern will fail schema validation","Null numeric values may affect interval filter results","Mismatched row_ids between primary and comparison datasets reduce keyed comparison effectiveness","Payment of 0.01 USDC not included or rejected causes 402 error"],"whenToPreferThis":"Choose this endpoint when you have pre-assembled time series completeness data (observed vs expected points) and need both numeric interval filtering and a side-by-side dataset comparison in a single API call. It is ideal for batch data quality checks up to 200 rows where you want two complementary analytical views without any external data fetching or predictions. Prefer this over general analytics tools when cost efficiency ($0.01 for two reports) and local computation on supplied data are priorities.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:15.111Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-interval-filter-keyed-dataset-d99b1de5"}