docs: fix broken documentation links (#1592)

Co-authored-by: Eduardo Lopez <5743857+loama@users.noreply.github.com>

Author: loama

docs/mintlify/imgs_models/StemGNN.png

@@ -27,7 +27,7 @@ of the prediction.
 
 - [Junyoung Chung, Caglar Gulcehre, KyungHyun Cho,
 Yoshua Bengio (2014). “Empirical Evaluation of Gated Recurrent Neural
-Networks on Sequence Modeling”.](https:arxivorg/abs/1412.3555)
+Networks on Sequence Modeling”.](https://arxiv.org/abs/1412.3555)
 - [Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, Yoshua Bengio
 (2014). “On the Properties of Neural Machine Translation:
 Encoder-Decoder Approaches”.](https://arxiv.org/abs/1409.1259)

docs/models.gru.html.md

@@ -11,7 +11,7 @@ is an
 [`MLP`](./models.mlp.html#mlp)-based
 deep neural architecture with backward and forward residual links. The
 network has two variants: (1) in its interpretable configuration,
-[`NBEATS`](./neuralforecast/models.nbeats.html#nbeats-2)
+[`NBEATS`](./models.nbeats.html#nbeats)
 sequentially projects the signal into polynomials and harmonic basis to
 learn trend and seasonality components; (2) in its generic
 configuration, it substitutes the polynomial and harmonic basis for

docs/models.nbeatsx.html.md

@@ -24,7 +24,7 @@ Zhang (2020). “Spectral Temporal Graph Neural Network for Multivariate
 Time-series
 Forecasting”.](https://proceedings.neurips.cc/paper/2020/hash/cdf6581cb7aca4b7e19ef136c6e601a5-Abstract.html)
 
-![Figure 1. StemGNN.](./imgs_models/stemgnn.png)
+![Figure 1. StemGNN.](./imgs_models/StemGNN.png)
 *Figure 1. StemGNN.*
 
 ## 1. StemGNN
@@ -115,4 +115,4 @@ ax.grid()
 
 ::: neuralforecast.models.stemgnn.StockBlockLayer
     options:
-      members: []
+      members: []

docs/models.stemgnn.html.md

@@ -350,7 +350,7 @@
     "\n",
     "### b. Tests and documentation\n",
     "\n",
-    "`nbdev` allows for testing and documenting the model during the development process. It allows users to iterate the development within the notebook, testing the code in the same environment. Refer to existing models, such as the complete MLP model [here](https://github.com/Nixtla/neuralforecast/blob/main/nbs/models.mlp.ipynb). These files already contain the tests, documentation, and usage examples that were used during the development process.\n",
+    "`nbdev` allows for testing and documenting the model during the development process. It allows users to iterate the development within the notebook, testing the code in the same environment. Refer to the existing MLP [implementation](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/models/mlp.py), [tests](https://github.com/Nixtla/neuralforecast/blob/main/tests/test_models/test_mlp.py), and [usage documentation](https://nixtlaverse.nixtla.io/neuralforecast/models.mlp.html) for a complete example of the model development process.\n",
     "\n",
     "### c. Export the new model to the library with `nbdev`\n",
     "\n",
@@ -369,7 +369,7 @@
     "Finally, add the model to the `core` class and additional files:\n",
     "\n",
     "1. Manually add the model in the following [init file](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/models/__init__.py).\n",
-    "2. Add the model to the `core` class, using the `nbdev` file [here](https://github.com/Nixtla/neuralforecast/blob/main/nbs/core.ipynb):\n",
+    "2. Add the model to the `core` class, using the source file [here](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py):\n",
     "    \n",
     "    a. Add the model to the initial model list:\n",
     "    ```python\n",
@@ -392,9 +392,9 @@
     "\n",
     "It's important to add the model to the necessary documentation pages so that everyone can find the documentation:\n",
     "\n",
-    "1. Add the model to the [model overview table](https://github.com/Nixtla/neuralforecast/blob/main/nbs/docs/capabilities/01_overview.ipynb).\n",
-    "2. Add the model to the [sidebar](https://github.com/Nixtla/neuralforecast/blob/main/nbs/sidebar.yml) for the API reference.\n",
-    "3. Add the model to [mint.json](https://github.com/Nixtla/neuralforecast/blob/main/nbs/mint.json)."
+    "1. Add the model to the [model overview table](https://github.com/Nixtla/neuralforecast/blob/main/nbs/docs/capabilities/overview.ipynb).\n",
+    "2. Add the model to the [documentation navigation](https://github.com/Nixtla/neuralforecast/blob/main/docs/mintlify/docs.json) for the API reference.\n",
+    "3. Confirm the page is present in [docs.json](https://github.com/Nixtla/neuralforecast/blob/main/docs/mintlify/docs.json)."
    ]
   },
   {
@@ -424,7 +424,7 @@
     "* Implement `init` and `forward` methods and set the class attributes.\n",
     "* Export model with `nbdev_export`.\n",
     "* Add model to this [init file](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/models/__init__.py).\n",
-    "* Add the model to the `core ` class [here](https://github.com/Nixtla/neuralforecast/blob/main/nbs/core.ipynb).\n",
+    "* Add the model to the `core` class [here](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py).\n",
     "* Follow the CONTRIBUTING guide to create the PR to upload the model.\n"
    ]
   }

nbs/docs/tutorials/adding_models.ipynb

@@ -1672,7 +1672,7 @@
    "source": [
     "This machine doesn't have GPU, but Google Colabs offers some for free. \n",
     "\n",
-    "Using [Colab's GPU to train NeuralForecast](https://nixtla.github.io/neuralforecast/examples/intermittentdata.html).\n"
+    "Using [Colab's GPU to train NeuralForecast](https://nixtlaverse.nixtla.io/neuralforecast/docs/tutorials/intermittent_data.html).\n"
    ]
   },
   {
@@ -2716,7 +2716,7 @@
    "id": "1394e95e-b65c-45d6-aea0-55e9e1940b60",
    "metadata": {},
    "source": [
-    "- [Available Models StatsForecast](../../../statsforecast/models.html)\n",
+    "- [Available Models StatsForecast](../../../statsforecast/src/core/models.html)\n",
     "- [Available Models NeuralForecast]( ../capabilities/overview.html)\n",
     "- [Loss Functions in NeuralForecast](../capabilities/objectives.html)\n",
     "- [Getting Started NeuralForecast](../getting-started/quickstart.html)\n",

nbs/docs/tutorials/comparing_methods.ipynb

@@ -616,7 +616,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We proposed an alternative model for long-horizon forecasting, the `NHITS`, based on feed-forward networks in (Challu et al. 2023). It achieves on par performance with `PatchTST`, with a fraction of the computational cost. The `NHITS` tutorial is available [here](https://nixtlaverse.nixtla.io/neuralforecast/docs/tutorials/longhorizon_with_nhits.html)."
+    "We proposed an alternative model for long-horizon forecasting, the `NHITS`, based on feed-forward networks in (Challu et al. 2023). It achieves on par performance with `PatchTST`, with a fraction of the computational cost. The `NHITS` tutorial is available [here](https://nixtlaverse.nixtla.io/neuralforecast/docs/tutorials/longhorizon_nhits.html)."
    ]
   },
   {