# Time Series Completeness — Adjacent Row Changes + Equal-Width Histogram

> Time Series Completeness — Adjacent Row Changes + Equal-Width Histogram 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) by computing adjacent row changes and an equal-width histogram in a single API call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-156?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-equal-width-histogram-24b9e7fc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Dq69vE-hPSBqjR-rcp0CG

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-equal-width-histogram-24b9e7fc -d '<json body>'
```

Example prompt: Can you run both an adjacent row changes report and an equal-width histogram (5 bins) on my time series completeness data for the 'sensor-feed' group — I have daily rows from 2024-01-01 to 2024-01-10 with observed_points and expected_points — and analyze the observed_points field?

## When to prefer this

Choose this endpoint when you need two complementary views of time series completeness data — sequential change detection (adjacent row changes) and distributional summarization (histogram) — in a single API call for up to 200 rows. It is ideal for data quality audits, pipeline monitoring, and completeness trend analysis where you already have the raw completeness records and need fast local analytics without any external data fetching or predictions.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- bins value outside 1–30 range — validation error
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Payment not included or insufficient USDC — 402 payment required response

## How this service works

Time series completeness — Adjacent row changes + Equal-width histogram: Adjacent row changes + Equal-width histogram 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 independent analytical reports: (1) an adjacent row changes report showing how observed or expected point counts shift between consecutive rows, and (2) an equal-width histogram with configurable bins (1–30) summarizing the distribution of the selected field across all supplied rows. Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "bins": {
   "type": "integer",
   "default": 5,
   "maximum": 30,
   "minimum": 1
  },
  "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-equal-width-histogram-24b9e7fc/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)