# Time Series Completeness: Trailing Mean + Missing-Value Fill

> Time Series Completeness: Trailing Mean + Missing-Value Fill 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).

Computes a trailing mean series and fills explicit missing values for time series completeness records (observed vs expected points) for up to 200 rows in two combined reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-154?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-trailing-mean-missing-value-fill-c63abb4c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_RaUCznYqVA4x9VgUECWH4

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-trailing-mean-missing-value-fill-c63abb4c -d '<json body>'
```

Example prompt: I have a batch of up to 50 daily completeness records with observed_points and expected_points for two groups — can you run a trailing mean with a window of 7 and fill any missing observed_points using the median, and give me both the smoothed series and the filled-value report?

## When to prefer this

Choose this endpoint when you need both a trailing mean smoothing series and a missing-value imputation report computed together in a single call for time series completeness data (observed vs expected points). It is ideal for lightweight, local batch analytics on up to 200 rows without any external lookups, predictions, or professional assessments — and the $0.01 flat fee covers both reports simultaneously.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — validation error
- Invalid date format (not YYYY-MM-DD) — schema rejection
- Window size smaller than 2 or larger than 200 — validation error
- observed_points or expected_points values outside ±1,000,000,000,000 — validation error
- Missing required fields (row_id, date, observed_points, expected_points) — schema error
- Invalid method enum value (not zero/mean/median) — schema rejection
- Invalid field enum value (not observed_points/expected_points) — schema rejection

## How this service works

Time series completeness — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill 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) a trailing mean series over the specified window for the chosen field (observed_points or expected_points), and (2) an explicit missing-value fill report where nulls are replaced using the chosen method (zero, mean, or median) — all computed locally against the supplied rows, with no external data lookup.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-trailing-mean-missing-value-fill-c63abb4c/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)