{"uid":"cap_xpqnWoMbp-g5zdeihWUah","slug":"numeric-precision-comparisons-trailing-mean-series-iqr-outlier-flags-6538df80","name":"Numeric Precision Comparisons — Trailing Mean Series + IQR Outlier Flags","description":"Numeric precision comparisons — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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-20-149?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_PrkGSaNCjQzgKRM8mp0o9","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 trailing moving-average series and IQR-based outlier flags for up to 200 rows of source/transformed numeric value pairs, delivering two local analytical reports per call.","exampleAgentPrompt":"I have 50 rows of source and transformed numeric values with dates — can you compute the trailing mean series using a window of 5 and flag any IQR outliers in the source_value field? Here's the data in row_id, dataset_group, date, source_value, transformed_value format.","exampleUseCases":[{"title":"ETL pipeline numeric drift detection","prompt":"I ran an ETL transformation on our sales figures and I want to check if any rows look like outliers. I have 120 rows with source_value and transformed_value for each date — can you compute a trailing mean series with window 10 on source_value and flag any IQR outliers so I can spot data drift?"},{"title":"Financial data quality audit","prompt":"We have a dataset of 80 transaction amounts before and after currency conversion — the source_value is in USD and transformed_value is the converted amount. Can you run trailing mean and IQR outlier detection with a window of 3 on the transformed_value field so we can catch any suspicious conversion errors?"},{"title":"Sensor reading anomaly check","prompt":"I've got 150 sensor readings split across two device groups, each row has a raw reading as source_value and a calibrated reading as transformed_value. Please compute the trailing mean series with a window of 7 and flag any IQR outliers in the source_value column so I can identify faulty sensors."}],"resultDescription":"Returns two analytical reports per call: (1) a trailing mean series computed over the selected field (source_value or transformed_value) using the specified window size, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range — all computed locally without external lookups or predictions.","failureModes":["Fewer than 1 row supplied — minimum 1 row required","More than 200 rows supplied — maximum limit exceeded","Invalid date format (must match YYYY-MM-DD pattern)","Missing required fields: row_id, dataset_group, date, source_value, or transformed_value","Window size outside allowed range of 2–200","Invalid field enum value — must be 'source_value' or 'transformed_value'","Numeric values outside allowed range (-1e12 to 1e12)","Payment not completed — 0.01 USDC required per call"],"whenToPreferThis":"Choose this endpoint when you need fast, local statistical analysis of numeric pairs (source vs transformed values) without any external data lookups — specifically when you want both a trailing moving average series and IQR outlier detection in one call at low cost ($0.01 USDC for up to 200 rows and 2 reports). It is ideal for data quality checks on ETL outputs, transformation validation, or time-series anomaly detection on small-to-medium datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:06.429Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-trailing-mean-series-iqr-outlier-flags-6538df80"}