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Sampling Observations — Trailing Mean Series + IQR Outlier Flags
x402
Sampling Observations — Trailing Mean Series + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a trailing mean time series and flags IQR-based outliers for supplied sampling observation records with sample values and weights.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied sampling observations records (sample_value, sample_weight). 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.
Output
Returns two statistical reports sharing local analytical engines: (1) a trailing mean series computed over the specified rolling window for the chosen field, and (2) IQR outlier flags identifying which observation rows fall beyond 1.5 times the interquartile range. Reports cover up to 200 input rows and include per-row identifiers and dataset group context.
Example use cases
Lab sample quality control review
“I've got 120 lab measurement records grouped by sample batch, each with a date, sample_value, and sample_weight. Run a trailing mean with a window of 5 on sample_value and flag any readings that are IQR outliers so I can spot bad measurements.”
Environmental sensor anomaly detection
“Here are 90 rows of daily air quality sensor readings with sample_value and sample_weight — please compute a 10-day trailing mean series and flag any days where the sample_value looks like an outlier based on IQR.”
Survey response trend smoothing
“I have 60 weekly survey response score records across two groups, group-a and group-b, with sample weights. Can you run a trailing mean with window size 4 on sample_weight and identify which weeks have outlier weights using IQR flagging?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular sampling observation data (up to 200 rows) and need both a smoothed trailing mean time series AND IQR-based outlier detection in a single low-cost call. It is ideal for quality control, sensor data review, lab measurements, or any periodic sampling workflow where trend visibility and anomaly flagging are both needed. Prefer it over generic statistics APIs when you have weighted samples with group labels and date-ordered rows.
Side effects
- Input array is empty or exceeds 200 rows — request rejected
- Window size larger than number of input rows — may produce incomplete or null leading values
- Missing required fields (row_id, date, sample_value, sample_weight, dataset_group) — validation error
- Date strings not matching YYYY-MM-DD format — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Payment of 0.01 USDC not completed — request blocked by x402 paywall
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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