# Time Series Completeness: Equal-Width Histogram + IQR Outlier Flags

> Time Series Completeness: Equal-Width Histogram + 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 2026-10-15).

Generates an equal-width histogram and IQR-based outlier flags for time series completeness data (observed vs expected points) across up to 200 rows, producing 2 independent analytical reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-170?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-equal-width-histogram-iqr-outlier-flags-d7540506
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Yi1SIPRzpv5yslQyC0bD5

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-equal-width-histogram-iqr-outlier-flags-d7540506 -d '<json body>'
```

Example prompt: Analyze the completeness of my time series data — I have 50 rows with date, dataset_group, observed_points, and expected_points fields. Use 10 bins and check observed_points for outliers. Flag any IQR outliers and give me a histogram distribution.

## When to prefer this

Choose this endpoint when you need fast, local statistical analysis of time series completeness records — specifically equal-width histogram distribution and IQR outlier detection — without requiring any external data lookup, live prediction, or human assessment. Ideal for data quality pipelines where you want to detect anomalous days or groups in observed vs expected point counts across up to 200 rows, at low cost ($0.01 USDC for 2 reports).

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Missing required fields (row_id, date, observed_points, or expected_points) in any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Bins value outside 1–30 range — rejected by schema
- All values are null for the selected field — insufficient data for IQR computation
- Invalid field enum value — only 'observed_points' or 'expected_points' accepted

## How this service works

Time series completeness — Equal-width histogram + IQR outlier flags: Equal-width histogram + 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 analytical reports: (1) an equal-width histogram showing the distribution of the selected field (observed_points or expected_points) across the specified number of bins, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range boundaries. No external lookups or predictions are made — all computation is local and deterministic.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "bins": {
   "type": "integer",
   "default": 5,
   "maximum": 30,
   "minimum": 1
  },
  "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-equal-width-histogram-iqr-outlier-flags-d7540506/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)