# Time Series Completeness — Category Frequencies + Trailing Mean Series

> Time Series Completeness — Category Frequencies + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Analyzes time series completeness records by computing category frequencies and a trailing mean series across up to 200 rows in two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-103?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-category-frequencies-trailing-mean-series-0d94f54d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FSJcb0omdYa6M6LH1Dznw

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-category-frequencies-trailing-mean-series-0d94f54d -d '<json body>'
```

Example prompt: Can you run a category frequency breakdown and trailing mean analysis (window size 7) on my time series completeness data — I have rows with dataset_group, date, observed_points, and expected_points — and analyze the observed_points field?

## When to prefer this

Choose this endpoint when you need two complementary views on time series completeness data — a categorical frequency summary by group plus a rolling/trailing mean series — delivered together in a single paid call. It is ideal for lightweight, local analytical computation on up to 200 rows of observed vs expected point counts without needing external data connections, predictions, or live lookups. Prefer it over more complex analytics platforms when cost-efficiency and self-contained computation on supplied data are priorities.

## Known failure modes

- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows supplied — exceeds maximum row limit
- Invalid date format (must match YYYY-MM-DD)
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points)
- Window size less than 2 or greater than 200 — out of allowed range
- Numeric field values outside allowed range (-1e12 to 1e12)
- Invalid field enum value — must be 'observed_points' or 'expected_points'

## How this service works

Time series completeness — Category frequencies + Trailing mean series: Category frequencies + Trailing mean 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 independent analytical reports: (1) a category frequency breakdown showing how often each dataset_group appears across the supplied rows, and (2) a trailing mean series computed over the specified rolling window for the selected numeric field (observed_points or expected_points). Both reports are derived locally from the 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-category-frequencies-trailing-mean-series-0d94f54d/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)