# Time Series Completeness: Numeric Interval Filter + IQR Outlier Flags

> Time Series Completeness: Numeric Interval Filter + 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 time series completeness records (observed vs expected data points) by filtering rows within a numeric interval and flagging IQR-based outliers across up to 200 rows in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-130?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-interval-filter-iqr-outlier-flags-2dbb057b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hYEeSe4LYHZmzLXKyd6pG

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-interval-filter-iqr-outlier-flags-2dbb057b -d '<json body>'
```

Example prompt: I have daily completeness records for two dataset groups from October 2026 — each row has observed_points and expected_points. Can you filter the observed_points field to only rows between 0 and 100, flag any IQR outliers, and give me two analytical reports across all 4 rows?

## When to prefer this

Choose this endpoint when you have structured time series completeness data (observed vs expected point counts) and need both numeric range filtering and IQR-based outlier detection in a single call, without requiring any external data sources, predictions, or live lookups. It is ideal for data quality monitoring workflows where you want two complementary statistical views — interval membership and distributional anomalies — on the same dataset for up to 200 rows at minimal cost.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not in YYYY-MM-DD format — schema pattern mismatch
- Minimum value greater than maximum value — logically invalid interval producing empty filter results
- Null numeric values in the target field — rows may be excluded from interval filtering

## How this service works

Time series completeness — Numeric interval filter + IQR outlier flags: Numeric interval filter + 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: one covering rows that fall within the specified numeric interval for the chosen field (observed_points or expected_points), and one flagging rows that are IQR-based statistical outliers. Each report operates on the same supplied dataset of up to 200 time series completeness rows without any external data 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-iqr-outlier-flags-2dbb057b/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)