# Time Series Completeness Deduplication and Numeric Ordering

> Time Series Completeness Deduplication and Numeric Ordering 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).

Deduplicates and numerically orders time series completeness records (observed_points, expected_points) for up to 200 rows across 2 independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-042?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-deduplication-and-numeric-ordering-6d816af6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_toXGrJP0hyjfgHFhYm4FP

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-deduplication-and-numeric-ordering-6d816af6 -d '<json body>'
```

Example prompt: I have a batch of time series completeness records with observed_points and expected_points for dataset groups — can you deduplicate exact duplicate rows and sort them by numeric order? I've got about 50 rows across groups like 'sensor-a' and 'sensor-b' with dates in 2025, and I want both the observed_points and expected_points field reports.

## When to prefer this

Choose this endpoint when you need lightweight, local deduplication and numeric ordering of time series completeness records (observed vs expected points) without any external data lookups or ML predictions. Ideal for data quality checks on small-to-medium batches (up to 200 rows) where exact duplicate removal and sorted ordering are the primary goals. Prefer alternatives if you need forecasting, anomaly detection, or completeness gap prediction.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, date, observed_points, expected_points) on any row
- Date field not in YYYY-MM-DD format — fails pattern validation
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Empty rows array — minItems constraint violated
- Payment not completed — x402 payment required before processing

## How this service works

Time series completeness — Exact row deduplication + Numeric row ordering: Exact row deduplication + Numeric row ordering 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 on the supplied time series completeness records: one covering exact row deduplication (identifying and removing exact duplicate rows) and one covering numeric ordering of the specified field (observed_points or expected_points). Both reports are 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-deduplication-and-numeric-ordering-6d816af6/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)