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    "# Validation"
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    "One of the primary challenges in time series forecasting is the inherent uncertainty and variability over time, making it crucial to validate the accuracy and reliability of the models employed. `TimeGPT` offers the possibility for cross-validation and historical forecasts to help you validate your predictions.\n",
    "\n",
    "### What You Will Learn\n",
    "\n",
    "1. **[Cross-Validation](https://docs.nixtla.io/docs/tutorials-cross_validation)**\n",
    "\n",
    "    - Learn how to perform time series cross-validation across different continuous windows of your data. \n",
    "\n",
    "2. **[Historical Forecasts](https://docs.nixtla.io/docs/tutorials-historical_forecast)**\n",
    "\n",
    "    - Generate in-sample forecasts to validate how `TimeGPT` would have performed in the past, providing insights into the model's accuracy. \n"
   ]
  }
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