# Time Series Completeness — Numeric Interval Filter + Trailing Mean Series

> Time Series Completeness — Numeric Interval Filter + 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 data (observed vs expected points) by applying a numeric interval filter and computing a trailing mean series, returning two independent analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-126?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-numeric-interval-filter-trailing-mean-series-d0cea022
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_RS9fq61PIKCRfHiPCuCYu

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-numeric-interval-filter-trailing-mean-series-d0cea022 -d '<json body>'
```

Example prompt: Take my completeness records for October 2026 — each row has observed_points and expected_points — filter them to only include rows where observed_points falls between 0 and 100, then compute a trailing mean with a window of 3, and give me both analytical reports.

## When to prefer this

Choose this endpoint when you need to simultaneously apply a numeric band filter and compute a rolling/trailing mean over time series completeness records (observed vs expected data points) in a single paid call. It is purpose-built for data quality and completeness monitoring workflows, delivering two analytical views from one request at low cost. Prefer it over generic analytics tools when your data is already structured as completeness records with dates and group labels, and you want both filtering and smoothing in one operation without setting up external computation.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row-level validation failure
- window value less than 2 or greater than 200 triggers schema validation error
- minimum greater than maximum produces an empty interval-filtered result set
- Missing required fields (row_id, date, observed_points, expected_points) cause row rejection
- Null values in observed_points or expected_points may result in NaN entries in trailing mean

## How this service works

Time series completeness — Numeric interval filter + Trailing mean series: Numeric interval filter + 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) records from the input dataset filtered to those where the selected field (observed_points or expected_points) falls within the specified numeric interval [minimum, maximum], and (2) a trailing mean series computed over the selected field using the specified rolling window size — both reports cover up to 200 input rows.

## 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
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-trailing-mean-series-d0cea022/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)