# Time Series Completeness — Numeric Row Ordering + IQR Outlier Flags

> Time Series Completeness — Numeric Row Ordering + 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 vs expected_points) to produce numeric row ordering and IQR-based outlier flags across two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-119?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-row-ordering-iqr-outlier-flags-112223bc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_I_LV9XXJsYuJvgmHbjT8D

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-row-ordering-iqr-outlier-flags-112223bc -d '<json body>'
```

Example prompt: Can you analyze my time series completeness data for observed_points across these 5 rows — row-1 through row-5 for group-alpha from 2025-01-01 to 2025-01-05, with expected_points of 100 each day and observed_points of 95, 102, 88, 200, 99 — and flag any IQR outliers while ranking the rows numerically?

## 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 numeric row ranking and IQR outlier flagging in a single call. It is ideal for data quality monitoring pipelines, ETL validation, and sensor completeness auditing where no external lookups or predictive modeling are needed — just fast, local statistical analysis on your supplied data.

## Known failure modes

- More than 200 rows submitted — input exceeds maxItems limit
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Null values in both observed_points and expected_points may reduce IQR reliability
- Invalid field enum value — must be 'observed_points' or 'expected_points'
- Payment not included or insufficient — x402 payment required at $0.01 USDC

## How this service works

Time series completeness — Numeric row ordering + IQR outlier flags: Numeric row ordering + 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 per call: (1) numeric ordering of the submitted rows by the selected field (observed_points or expected_points), and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. No external lookups or predictions are made — all analysis is performed on the supplied data only.

## 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-row-ordering-iqr-outlier-flags-112223bc/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)