# Time Series Completeness — Numeric Row Ordering + Numeric Interval Filter

> Time Series Completeness — Numeric Row Ordering + Numeric Interval Filter 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).

Analyzes time series completeness records (observed vs expected points) using numeric row ordering and interval filtering, producing 2 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-113?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-row-ordering-numeric-interval-filter-f25e8dbb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Z4Ixhj3jnJCyx1K0tFejU

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-numeric-interval-filter-f25e8dbb -d '<json body>'
```

Example prompt: Check my time series completeness data for dataset groups 'sensors-a' and 'sensors-b' across October 2026 — order the rows numerically by observed_points and filter to rows where observed_points falls between 5 and 50. I have up to 20 rows to analyze.

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records (observed vs expected data points) and need lightweight local analytical reports — specifically numeric ordering and interval filtering — without requiring any live data lookup, external enrichment, or predictive modeling. Best suited for batch offline QA checks on up to 200 rows across one or two dataset groups, where the $0.01 flat fee covers both reports in a single call.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit
- Row missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (not YYYY-MM-DD) — schema pattern rejection
- minimum greater than maximum — logically empty filter interval producing no results
- observed_points or expected_points outside allowed range (±1 trillion) — schema validation error

## How this service works

Time series completeness — Numeric row ordering + Numeric interval filter: Numeric row ordering + Numeric interval filter 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 2 analytical reports on the supplied time series completeness rows: one applying numeric row ordering (sorting rows by the selected field) and one applying a numeric interval filter (retaining only rows where the selected field falls within the specified minimum–maximum range). Both reports operate on the same input batch of up to 200 rows using local analytical engines, with no external data lookup or prediction.

## 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-row-ordering-numeric-interval-filter-f25e8dbb/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)