HubVibe BigQuery Anomaly Detection

x402

HubVibe BigQuery Anomaly Detection is a paid API for AI agents from hubvibe-io.com, paid per call via x402, $10/call, status unknown (last checked 2026-10-02).

Detects anomalies in a BigQuery time series table by comparing it against a history table using Google's AI.DETECT_ANOMALIES (TimesFM) model.

Price / call$10

Seller volume →—

Success—

StatusUnknown

How this service works

Anomaly detection on a time series: score recent periods against history with BigQuery AI.DETECT_ANOMALIES (TimesFM). Give one table and its latest periods are scored against its own history, or a history and a target table. Returns every checked row with bounds, anomaly flag and probability, plus the anomaly count. Input: history_table, target_table, timestamp_col, data_col; optional target_last, threshold, id_cols.

Output

Returns rows from the target table that are flagged as anomalous, along with their anomaly probability scores as computed by Google's TimesFM model via BigQuery AI.DETECT_ANOMALIES. Results may be segmented per series if ID columns are specified.

Example use cases

E-commerce revenue spike detection

“Scan my BigQuery table `shop.sales.daily_revenue_2024` for anomalies against the historical baseline in `shop.sales.daily_revenue_2023` — use `sale_date` as the timestamp and `total_revenue` as the value column, and flag anything above 0.95 anomaly probability.”

IoT sensor fault monitoring

“Run anomaly detection on my sensor readings table `iot.prod.temperature_readings` compared to `iot.prod.temperature_history`, with `reading_ts` as the timestamp, `temp_celsius` as the value, grouping by `sensor_id`, and flag readings with anomaly probability over 0.99.”

Website traffic anomaly audit

“Check `analytics.web.hourly_sessions_this_month` for unusual traffic patterns against `analytics.web.hourly_sessions_last_year` — timestamp column is `hour_start`, value column is `session_count`, and use a 0.9 anomaly probability threshold.”

Technical details

Endpoint

POSThttps://hubvibe-io.com/work/data/anomalies?utm_source=zero.xyz

When to prefer

Choose this endpoint when you have time series data already stored in BigQuery and want to leverage Google's pretrained TimesFM model for anomaly detection without building your own ML pipeline. It is ideal when you have a clear historical baseline table and a recent target table with matching schema. Prefer this over generic anomaly detection APIs when your data volume justifies BigQuery-scale processing and you want probabilistic anomaly scores with configurable thresholds.

Side effects

Invalid or inaccessible BigQuery table reference (project.dataset.table format required)
Mismatched timestamp or value column names between target and history tables
Anomaly probability threshold outside 0.5–0.999 range
max_scan_gib exceeded causing query abort
Tables with insufficient historical data for TimesFM to produce reliable anomaly scores
Payment failure or insufficient USDC balance ($10 per call)

Input

Schema · Request body

Field	Type	Description
id_cols	array
data_col	string
language	string
target_last	integer
max_scan_gib	number
target_table	string
history_table	string
timestamp_col	string
anomaly_prob_threshold	number

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": {
      "mode": "split_by_time",
      "rows": [
        {
          "date": "2023-03-20",
          "is_anomaly": "false",
          "state_name": "Texas",
          "confirmed_cases": "8631000",
          "anomaly_probability": "0.12"
        }
      ],
      "columns": [
        "state_name",
        "date",
        "confirmed_cases",
        "is_anomaly",
        "lower_bound",
        "upper_bound",
        "anomaly_probability"
      ],
      "data_col": "confirmed_cases",
      "row_count": 10,
      "target_table": "bigquery-public-data.covid19_nyt.us_states",
      "anomaly_count": 2,
      "gib_processed": 0.012,
      "history_table": "bigquery-public-data.covid19_nyt.us_states",
      "timestamp_col": "date",
      "target_periods": 30,
      "anomaly_prob_threshold": 0.95
    },
    "status": "ok",
    "worker": "data.anomalies",
    "price_usd": 10,
    "provenance": {
      "steps": [
        {
          "ms": 312,
          "ok": true,
          "step": "quote",
          "reason": "provider_timeout",
          "provider": "coinbase-advanced-trade-public"
        }
      ],
      "attempts": 1,
      "elapsed_ms": 340,
      "providers_used": [
        "coinbase-advanced-trade-public"
      ]
    },
    "receipt_id": "rcpt_9f1c2b3a4d5e6f70",
    "attribution": [
      {
        "url": "https://translate.google.com",
        "text": "Translated by Google"
      }
    ],
    "receipt_url": "/work/receipts/rcpt_9f1c2b3a4d5e6f70"
  }
}

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