# Time Series Completeness Analytics: Equal-Width Histogram + Date-by-Category Pivot

> Time Series Completeness Analytics: Equal-Width Histogram + 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 2026-10-19).

Analyzes time series completeness records by generating an equal-width histogram and a date-by-category pivot table for observed vs. expected data points, supporting up to 200 rows in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-173?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-analytics-equal-width-histogram-date-by-326b4309
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_J2L8aR36yafYxhWvDTJEV

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-analytics-equal-width-histogram-date-by-326b4309 -d '<json body>'
```

Example prompt: I have 4 time series completeness records — row-1 (group-a, 2026-10-01, observed 10, expected 1), row-2 (group-b, 2026-10-02, observed 20, expected 2), row-3 (group-a, 2026-10-03, observed 15, expected 3), row-4 (group-b, 2026-10-04, observed 30, expected 4) — can you run an equal-width histogram with 5 bins on the observed_points field and also give me a date-by-category pivot for those completeness records?

## When to prefer this

Choose this endpoint when you need both a histogram distribution view and a date-by-category pivot on the same time series completeness dataset in a single paid call. It is ideal for data engineers and analysts who supply their own completeness records (observed vs. expected points) and need offline statistical summaries without any external data fetching, prediction, or live lookup. It is not suitable for real-time monitoring, large datasets exceeding 200 rows, or use cases requiring professional assessment.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (non-YYYY-MM-DD) causes row validation failure
- bins value outside 1–30 range triggers schema validation error
- Missing required fields (row_id, date, observed_points, expected_points) causes row rejection
- All null values in the selected field may produce an empty or degenerate histogram
- Insufficient USDC balance results in payment failure before processing

## How this service works

Time series completeness — Equal-width histogram + Date by category pivot: Equal-width histogram + 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 independent analytical reports: (1) an equal-width histogram with the specified number of bins (1–30) showing the frequency distribution of the selected field (observed_points or expected_points) across the supplied rows, and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group. 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-analytics-equal-width-histogram-date-by-326b4309/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)