fix links in how-to-guides

Author: deven367

nbs/docs/how-to-guides/Exogenous.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "## Prerequisites\n",
     "\n",
-    "This tutorial assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](../getting-started/1_Getting_Started_short)\n",
+    "This tutorial assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](../getting-started/getting_started_short.html)\n",
     ":::"
    ]
   },
@@ -32,7 +32,7 @@
     "\n",
     "**Exogenous regressors** are variables that can affect the values of a time series. They may not be directly related to the variable that is being forecasted, but they can still have an impact on it. Examples of exogenous regressors are weather data, economic indicators, or promotional sales. They are typically collected from external sources and by incorporating them into a forecasting model, they can improve the accuracy of our predictions. \n",
     "\n",
-    "By the end of this tutorial, you'll have a good understanding of how to incorporate exogenous regressors into [StatsForecast](../../models)'s models. Furthermore, you'll see how to evaluate their performance and decide whether or not they can help enhance the forecast. "
+    "By the end of this tutorial, you'll have a good understanding of how to incorporate exogenous regressors into [StatsForecast](../../src/core/models.html)'s models. Furthermore, you'll see how to evaluate their performance and decide whether or not they can help enhance the forecast. "
    ]
   },
   {
@@ -74,7 +74,7 @@
    "id": "91cea3f0-d72b-4015-9c23-6009b63a493f",
    "metadata": {},
    "source": [
-    "We assume that you have StatsForecast already installed. If not, check this guide for instructions on [how to install StatsForecast](../getting-started/0_Installation)."
+    "We assume that you have StatsForecast already installed. If not, check this guide for instructions on [how to install StatsForecast](../getting-started/installation.html)."
    ]
   },
   {
@@ -137,7 +137,7 @@
    "id": "4481392d-59a9-4179-b28b-7179a551bb8b",
    "metadata": {},
    "source": [
-    "We can plot the sales of this product-store combination with the `statsforecast.plot` method from the [StatsForecast](../../core#class-statsforecast) class. This method has multiple parameters, and the required ones to generate the plots in this notebook are explained below. \n",
+    "We can plot the sales of this product-store combination with the `statsforecast.plot` method from the [StatsForecast](../../src/core/core.html#statsforecast) class. This method has multiple parameters, and the required ones to generate the plots in this notebook are explained below. \n",
     "\n",
     "- `df`: A pandas dataframe with columns [`unique_id`, `ds`, `y`].\n",
     "- `forecasts_df`: A pandas dataframe with columns [`unique_id`, `ds`] and models.\n",

nbs/docs/how-to-guides/sklearn_models.ipynb

@@ -161,7 +161,7 @@
    "id": "b286dc48-003c-47f4-bee1-54efde21578f",
    "metadata": {},
    "source": [
-    "The utilsforecast library [provides some utilies for feature engineering](../../../utilsforecast/feature_engineering)."
+    "The utilsforecast library [provides some utilies for feature engineering](../../../utilsforecast/feature_engineering.html)."
    ]
   },
   {