# Time Series Completeness — IQR Outlier Flags + Secondary-Measure Weighted Mean

> Time Series Completeness — IQR Outlier Flags + Secondary-Measure Weighted Mean 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).

Analyzes time series completeness records (observed vs expected data points) using IQR-based outlier detection and secondary-measure weighted mean, delivering two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-177?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-iqr-outlier-flags-secondary-measure-weighted-661e950d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7ubTHtn6EuM9UhOAIIOFl

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability time-series-completeness-iqr-outlier-flags-secondary-measure-weighted-661e950d -d '<json body>'
```

Example prompt: Run IQR outlier detection and secondary-measure weighted mean on my completeness records — I have 5 rows: 2024-01-01 row-1 group-a observed=95 expected=100, 2024-01-02 row-2 group-a observed=20 expected=100, 2024-01-03 row-3 group-b observed=98 expected=100, 2024-01-04 row-4 group-b observed=97 expected=100, 2024-01-05 row-5 group-a observed=5 expected=100 — use observed_points as the primary field.

## When to prefer this

Choose this endpoint when you need to statistically identify outlier dates or rows in a time series completeness dataset using IQR methodology alongside a weighted mean, without any external data lookup or live prediction. Ideal for data quality pipelines, SLA reporting, or ETL monitoring where you have up to 200 completeness records (observed vs expected counts) and want two complementary statistical views in a single paid call at $0.01 USDC.

## Known failure modes

- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- All null values for the selected field prevent IQR computation
- Single-row input may produce degenerate statistics
- Non-numeric observed_points or expected_points cause type errors

## How this service works

Time series completeness — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean 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.

## Output

Returns two analytical reports: (1) IQR outlier flags for each row indicating whether the specified field (observed_points or expected_points) is a statistical outlier based on interquartile range, and (2) a secondary-measure weighted mean computed across the supplied rows. Both reports are derived entirely from the supplied data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-iqr-outlier-flags-secondary-measure-weighted-661e950d/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)