# Time Series Completeness — Exact Row Deduplication + Adjacent Row Changes

> Time Series Completeness — Exact Row Deduplication + 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 up to 200 time series completeness records to produce two reports: exact duplicate row detection and adjacent-row change detection for observed_points or expected_points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-046?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-exact-row-deduplication-adjacent-row-changes-dcdf9ade
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_fWCFixyG35YO2QHghLeIL

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-exact-row-deduplication-adjacent-row-changes-dcdf9ade -d '<json body>'
```

Example prompt: Can you run exact row deduplication and adjacent row change detection on my completeness data? I have rows with row_id, dataset_group, date, observed_points, and expected_points — up to about 50 rows — and I want to analyze the observed_points field.

## When to prefer this

Choose this endpoint when you need simultaneous exact-duplicate detection and adjacent-row change analysis on a time series completeness dataset of up to 200 rows, at a fixed cost of $0.01 USDC for both reports together. It is ideal for data quality audits, pipeline pre-ingestion checks, or monitoring completeness logs where both deduplication and change detection are needed in one call without any external dependencies or live lookups.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Numeric values outside the allowed range cause schema validation failure
- Empty rows array (minItems:1 not met) results in an error

## How this service works

Time series completeness — Exact row deduplication + Adjacent row changes: Exact row deduplication + 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: (1) an exact row deduplication report identifying rows with identical field values across the supplied records, and (2) an adjacent row change report highlighting where consecutive rows differ in the selected field (observed_points or expected_points). Both reports are computed locally from supplied data with no 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-exact-row-deduplication-adjacent-row-changes-dcdf9ade/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)