# Time Series Completeness — Numeric Distribution + IQR Outlier Flags

> Time Series Completeness — Numeric Distribution + 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 2026-10-19).

Analyzes up to 200 time series completeness records (observed_points, expected_points) and returns numeric distribution statistics plus IQR-based outlier flags in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-080?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-numeric-distribution-iqr-outlier-flags-d9e92875
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_CNb2P_SuIreoNBmFpsZeQ

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-numeric-distribution-iqr-outlier-flags-d9e92875 -d '<json body>'
```

Example prompt: Can you run a numeric distribution analysis and IQR outlier detection on my time series completeness data? I have records for group-a and group-b from 2026-10-01 to 2026-10-04 with observed_points values like 10, 20, 15, 30 and expected_points 1, 2, 3, 4 — analyze the observed_points field across all 4 rows.

## When to prefer this

Choose this endpoint when you have up to 200 time series completeness records (observed vs expected data points) and need both a numeric distribution summary and IQR-based outlier detection in a single low-cost call. It is ideal for data quality monitoring workflows where you want statistical characterization and anomaly flagging of completeness metrics without any external data lookup or live prediction. It delivers two reports per call for $0.01 USDC, making it cost-effective for batch completeness audits.

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum item limit
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Non-numeric or out-of-range values for observed_points or expected_points — rejection
- Invalid field enum value (must be 'observed_points' or 'expected_points') — error response
- Payment not included or insufficient — x402 payment required response

## How this service works

Time series completeness — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags 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 for the specified field (observed_points or expected_points): (1) a numeric distribution summary including min, max, mean, median, standard deviation, and quartiles; and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range bounds. Both reports are computed locally 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-numeric-distribution-iqr-outlier-flags-d9e92875/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)