100_uncertainty_quantification.ipynb

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    "# Uncertainty quantification\n",
    "\n",
    "In forecasting, it is essential to consider the full distribution of predictions rather than only a point prediction. This approach allows for a better understanding of the uncertainty surrounding the forecast. `TimeGPT` supports uncertainty quantification through quantile forecasts and prediction intervals.\n",
    "\n",
    "### What You Will Learn\n",
    "\n",
    "1. **[Quantile Forecasts](https://docs.nixtla.io/docs/tutorials-quantile_forecasts)**\n",
    "\n",
    "    - Learn how to compute specific quantiles of the forecast distribution using `TimeGPT`. \n",
    "\n",
    "2. **[Prediction Intervals](https://docs.nixtla.io/docs/tutorials-prediction_intervals)**\n",
    "\n",
    "    - Learn how to generate prediction intervals with `TimeGPT`, which give you a range of values that the forecast can take with a given probability. \n"
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