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    "# Online (Real-Time) Anomaly Detection"
   ]
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   "source": [
    "Online anomaly detection dynamically identifies anomalies as data streams in, allowing users to specify the number of timestamps to monitor. This method is well-suited for immediate applications, such as fraud detection, live sensor monitoring, or tracking real-time demand changes. By focusing on recent data and continuously generating forecasts, it enables timely responses to anomalies in critical scenarios.\n",
    "\n",
    "This section provides various recipes for performing real-time anomaly detection using TimeGPT, offering users the ability to detect outliers and unusual patterns as they emerge, ensuring prompt intervention in time-sensitive situations.\n",
    "\n",
    "This section covers:\n",
    "\n",
    "* [Online anomaly detection](https://docs.nixtla.io/docs/capabilities-online-anomaly-detection-quickstart)\n",
    "\n",
    "* [How to adjust the detection process](https://docs.nixtla.io/docs/capabilities-online-anomaly-detection-adjusting_detection_process.ipynb)\n",
    "\n",
    "* [Univariate vs. multiseries anomaly detection](https://docs.nixtla.io/docs/capabilities-online-anomaly-detection-univariate_vs_multivariate_anomaly_detection)\n"
   ]
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