# Time Series Completeness — Numeric Interval Filter + Explicit Missing-Value Fill

> Time Series Completeness — Numeric Interval Filter + 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 2026-10-15).

Filters time series completeness records by a numeric interval and fills missing values using zero, mean, or median imputation — returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-135?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-numeric-interval-filter-explicit-missing-4fa5b861
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_fPAl9qvFAbUn2bKZos58w

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-explicit-missing-4fa5b861 -d '<json body>'
```

Example prompt: Can you filter my time series completeness records so that only rows where observed_points falls between 0 and 100 are kept, fill any missing observed_points using median imputation, and give me two analytical reports? I have up to 50 rows with fields like row_id, dataset_group, date, observed_points, and expected_points.

## When to prefer this

Choose this endpoint when you need to simultaneously clean a time series completeness dataset by numeric range filtering AND impute missing values in a single call, receiving two analytical reports as output. It is ideal for batch offline analysis of up to 200 rows of observed/expected point records where no live data lookup, prediction, or external enrichment is needed. Prefer it over general-purpose analytics when your use case specifically involves completeness metrics (observed vs. expected points) with null-handling requirements.

## Known failure modes

- Input array is empty or exceeds 200 rows — validation error returned
- date field does not match YYYY-MM-DD pattern — rejected with schema error
- minimum value is greater than maximum — may produce empty filtered result with no error
- observed_points or expected_points exceeds allowed numeric range — schema rejection
- missing required fields (row_id, date) — validation failure
- invalid fill method or field enum value — schema rejection

## How this service works

Time series completeness — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + 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

The endpoint returns two independent analytical reports computed from the filtered and imputed dataset. Each report reflects completeness metrics (observed vs. expected points) after applying the numeric interval filter to exclude out-of-range rows and replacing null values in the target field using the chosen fill method (zero, mean, or median). Results are based entirely on the supplied data — no external lookups or live data.

## 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"
  },
  "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-explicit-missing-4fa5b861/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)