# Time Series Completeness — IQR Outlier Flags + Explicit Missing-Value Fill

> Time Series Completeness — IQR Outlier Flags + Explicit Missing-Value Fill 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 points) using IQR-based outlier detection and explicit missing-value filling, returning two independent analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-180?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-iqr-outlier-flags-explicit-missing-value-fill-a97b3234
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6UI9DsMeRSTKg4GrbBmB7

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-explicit-missing-value-fill-a97b3234 -d '<json body>'
```

Example prompt: I have 50 rows of daily completeness records with observed_points and expected_points from 2024-01-01 to 2024-02-19 across two dataset groups — can you flag IQR outliers and fill any missing observed_points using the median, then give me both reports?

## When to prefer this

Choose this endpoint when you need both IQR-based outlier detection AND missing-value imputation applied to time series completeness data in a single paid call. It is particularly suited for data quality workflows involving observed vs expected point counts grouped by date and category, with up to 200 rows. It is purely local/analytical — no predictions, live data lookups, or external actions are performed. Prefer it over single-report endpoints when you need both outlier flags and filled data simultaneously at low cost ($0.01 for both reports).

## Known failure modes

- Exceeding 200 rows returns an error or truncation
- Invalid date format (not YYYY-MM-DD) causes validation failure
- Missing required fields (row_id, observed_points, expected_points) cause rejection
- Providing all-null values for the target field may produce degenerate fill results
- Non-numeric values in observed_points or expected_points fail schema validation
- Empty rows array is rejected (minItems: 1)

## How this service works

Time series completeness — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill 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

Two independent analytical reports: (1) IQR outlier flag report identifying which rows have outlying values in the specified field across all rows, and (2) an explicit missing-value fill report where null/missing values in the specified field are replaced using the chosen method (zero, mean, or median). Results are scoped to the supplied rows (up to 200) and computed locally without 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-iqr-outlier-flags-explicit-missing-value-fill-a97b3234/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)