# Time Series Completeness — Numeric Row Ordering & Adjacent Row Changes

> Time Series Completeness — Numeric Row Ordering & Adjacent Row Changes 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 data (observed vs expected points) to produce two reports: numeric row ordering and adjacent row changes, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-116?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-adjacent-row-changes-b1fa12b1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_TDncSzzfscPplEwIDTwcs

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-adjacent-row-changes-b1fa12b1 -d '<json body>'
```

Example prompt: Can you check my time series completeness data for anomalies? I have up to 200 rows with dates, dataset groups, and observed vs expected point counts — I want both a numeric ordering report and an adjacent row change report based on the observed_points field.

## When to prefer this

Choose this endpoint when you need lightweight, deterministic completeness diagnostics on a small time series dataset (up to 200 rows) without requiring external data sources, live predictions, or professional assessment. It is ideal for quick QA passes on ingestion pipelines, sensor feeds, or any scenario where you want both a ranked ordering and a day-over-day delta report from the same call at minimal cost ($0.01 USDC for both reports).

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Dates not matching YYYY-MM-DD format fail pattern validation
- Null values in the selected field may produce incomplete change calculations
- Invalid field enum value (not 'observed_points' or 'expected_points') causes a schema error

## How this service works

Time series completeness — Numeric row ordering + Adjacent row changes: Numeric row ordering + Adjacent row changes 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 analytical reports computed locally: (1) a numeric row ordering of the supplied time series rows ranked by the selected field (observed_points or expected_points), and (2) an adjacent row changes report showing the delta between consecutive rows — all based solely on the supplied data with no external lookups or predictions.

## 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-numeric-row-ordering-adjacent-row-changes-b1fa12b1/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)