# Time Series Completeness IQR Outlier Flags + Two-Measure Correlation

> Time Series Completeness IQR Outlier Flags + Two-Measure Correlation 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 up to 200 rows of time series completeness data (observed vs expected points) using IQR-based outlier detection and two-measure correlation, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-176?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-two-measure-correlation-afcfacbd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Wkv44XJSJuamD1QEAbg8_

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-two-measure-correlation-afcfacbd -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and two-measure correlation on my completeness records? I have up to 50 rows with date, dataset_group, observed_points, and expected_points columns — I want to flag any anomalies in the observed_points field.

## When to prefer this

Choose this endpoint when you need a low-cost, self-contained statistical analysis of time series completeness data (observed vs expected points) without any external data lookups or live predictions. It is ideal for data quality audits, pipeline monitoring, and QA workflows where you already have the completeness records and need fast IQR outlier flags plus a correlation report in a single call for up to 200 rows.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Dates not matching YYYY-MM-DD pattern will fail schema validation
- Too few rows for meaningful IQR computation (single-row input produces degenerate results)
- Null values in numeric fields may affect outlier and correlation calculations

## How this service works

Time series completeness — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation 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 independent analytical reports: (1) IQR-based outlier flags for each row indicating whether the selected measure (observed_points or expected_points) is an outlier relative to the interquartile range, and (2) a two-measure correlation report comparing observed_points and expected_points across the submitted rows. All computation is performed locally on 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-two-measure-correlation-afcfacbd/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)