# Time Series Completeness — Adjacent Row Changes & Date-by-Category Pivot

> Time Series Completeness — Adjacent Row Changes & Date-by-Category Pivot 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 to produce two reports: adjacent row change analysis and a date-by-category pivot table, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-160?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-adjacent-row-changes-date-by-category-pivot-beb2cd3c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_KYSz8m7YtpFc_eyp9E8NH

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-adjacent-row-changes-date-by-category-pivot-beb2cd3c -d '<json body>'
```

Example prompt: Can you run two completeness reports on my time series data — one showing adjacent row changes and one pivoting observed_points by date and dataset group? Here are the rows: [{"row_id":"r1","dataset_group":"sensors","date":"2025-01-01","observed_points":95,"expected_points":100},{"row_id":"r2","dataset_group":"sensors","date":"2025-01-02","observed_points":88,"expected_points":100}].

## When to prefer this

Choose this endpoint when you need two complementary completeness reports — adjacent row change analysis and date-by-category pivot — in a single low-cost call ($0.01 USDC) on up to 200 rows of time series completeness data. Ideal for data quality audits, ETL monitoring, and reporting workflows where you already have the completeness records and need fast local analytical computation without any external data lookup or live prediction.

## Known failure modes

- Input rows exceed 200 item limit — request will be rejected
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation failure
- Invalid date format (not YYYY-MM-DD) triggers pattern validation error
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected
- Payment of 0.01 USDC not fulfilled — endpoint returns 402
- Empty rows array not accepted (minItems: 1)

## How this service works

Time series completeness — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot 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 two analytical reports: (1) an adjacent row changes report showing how observed_points or expected_points shift between consecutive rows, and (2) a date-by-category pivot table summarizing the selected metric across dataset groups and dates — all computed locally from the supplied data, 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-adjacent-row-changes-date-by-category-pivot-beb2cd3c/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)