# Time Series Completeness: Adjacent Row Changes + Base-100 Series

> Time Series Completeness: Adjacent Row Changes + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Computes adjacent row-to-row changes and a base-100 indexed series for up to 200 time series completeness records (observed vs expected points) in a single paid API call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-155?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-adjacent-row-changes-base-100-series-ed463e77
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_UEbGYKqpNm-l5tR54zrnL

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-base-100-series-ed463e77 -d '<json body>'
```

Example prompt: Can you run the adjacent row changes and base-100 series analysis on my completeness data — I have daily rows for groups 'sensor-a' and 'sensor-b' with observed_points and expected_points from 2025-01-01 to 2025-01-07, and I want the series indexed starting from row 0 using the observed_points field?

## When to prefer this

Choose this endpoint when you need two fast, self-contained analytical reports — adjacent changes and base-100 indexing — on time series completeness data (observed vs expected points) without any external lookups or predictions. It is ideal when you already have your completeness records structured and simply need quantitative change and index analytics delivered together in one low-cost call (0.01 USDC for both reports). Prefer alternatives if you need forecasting, anomaly detection beyond simple deltas, or more than 200 rows processed at once.

## Known failure modes

- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- More than 200 rows submitted returns an error due to item limit
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- base_index out of range (>= number of rows submitted) causes indexing failure
- Null values in the selected field may produce undefined change or index results
- Payment failure (insufficient USDC balance) prevents the call from executing

## How this service works

Time series completeness — Adjacent row changes + Base-100 series: Adjacent row changes + Base-100 series 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) adjacent row changes showing the difference between each consecutive row's selected metric value, and (2) a base-100 indexed series rebasing the selected field relative to the specified base_index row. Both reports are computed locally on the supplied rows (up to 200) with no external data 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-adjacent-row-changes-base-100-series-ed463e77/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)