[CHORE] missed fixes (#1505)

Author: deven367

docs/models.nbeatsx.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)
+[`NBEATS`](./neuralforecast/models.nbeats.html#nbeats-2)
 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

nbs/docs/tutorials/comparing_methods.ipynb

@@ -32,7 +32,7 @@
     "\n",
     "We will train an assortment of models from various forecasting paradigms:\n",
     "\n",
-    "*[StatsForecast]((https://github.com/Nixtla/statsforecast))*\n",
+    "*[StatsForecast](https://github.com/Nixtla/statsforecast)*\n",
     "\n",
     "- Baseline models: These models are simple yet often highly effective for providing an initial perspective on the forecasting problem. We will use `SeasonalNaive` and `HistoricAverage` models for this category.\n",
     "- Intermittent models: For series with sporadic, non-continuous demand, we will utilize models like `CrostonOptimized`, `IMAPA`, and `ADIDA`. These models are particularly suited for handling zero-inflated series.\n",

nbs/docs/tutorials/forecasting_tft.ipynb

@@ -453,7 +453,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `cross_validation` method allows you to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods. See [this tutorial](https://nixtlaverse.nixtla.io/statsforecast/examples/getting_started_complete.html) for an animation of how the windows are defined. \n",
+    "The `cross_validation` method allows you to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods. See [this tutorial](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_complete.html) for an animation of how the windows are defined. \n",
     "\n",
     "With time series data, cross validation is done by defining a sliding window across the historical data and predicting the period following it. This form of cross validation allows us to arrive at a better estimation of our model’s predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models. The `cross_validation` method will use the validation set for hyperparameter selection, and will then produce the forecasts for the test set.\n",
     "\n",

nbs/docs/tutorials/intermittent_data.ipynb

@@ -60,7 +60,7 @@
    "source": [
     "## 1. Install libraries \n",
     "\n",
-    "We assume that you have NeuralForecast already installed. If not, check this guide for instructions on [how to install NeuralForecast](https://nixtlaverse.nixtla.io/neuralforecast/examples/installation.html) \n",
+    "We assume that you have NeuralForecast already installed. If not, check this guide for instructions on [how to install NeuralForecast](https://nixtlaverse.nixtla.io/neuralforecast/docs/getting-started/installation.html) \n",
     "\n",
     "Install the necessary packages using `pip install neuralforecast`"
    ]
@@ -135,7 +135,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Plot some series using the plot method from the `StatsForecast` class. This method prints 8 random series from the dataset and is useful for basic [EDA](https://nixtlaverse.nixtla.io/statsforecast/core.html#statsforecast.plot)."
+    "Plot some series using the plot method from the `StatsForecast` class. This method prints 8 random series from the dataset and is useful for basic [EDA](https://nixtlaverse.nixtla.io/statsforecast/src/core/core.html#statsforecast.plot)."
    ]
   },
   {

nbs/docs/tutorials/longhorizon_transformers.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/examples/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_with_nhits.html)."
    ]
   },
   {

nbs/docs/tutorials/using_mlflow.ipynb

@@ -22,7 +22,7 @@
    "id": "963311a3-2427-4694-981b-438fce1c2981",
    "metadata": {},
    "source": [
-    "To install Neuralforecast refer to [Installation](../getting-started/04_installation).\n",
+    "To install Neuralforecast refer to [Installation](../getting-started/installation.html).\n",
     "\n",
     "To install mlflow: `pip install mlflow`"
    ]

nbs/docs/use-cases/electricity_peak_forecasting.ipynb

@@ -208,7 +208,7 @@
    "source": [
     "We fit the model by instantiating a `NeuralForecast` object with the following required parameters:\n",
     "\n",
-    "* `models`: a list of models. Select the models you want from [models]( ../capabilities/overview.html.ipynb) and import them.\n",
+    "* `models`: a list of models. Select the models you want from [models]( ../capabilities/overview.html) and import them.\n",
     "\n",
     "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)"
    ]
@@ -231,7 +231,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `cross_validation` method allows the user to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods. This method re-trains the model and forecast each window. See [this tutorial](https://nixtlaverse.nixtla.io/statsforecast/examples/getting_started_complete.html) for an animation of how the windows are defined. \n",
+    "The `cross_validation` method allows the user to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods. This method re-trains the model and forecast each window. See [this tutorial](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_complete.html) for an animation of how the windows are defined. \n",
     "\n",
     "Use the `cross_validation` method to produce all the daily forecasts for September. To produce daily forecasts set the forecasting horizon `h` as 24. In this example we are simulating deploying the pipeline during September, so set the number of windows as 30 (one for each day). Finally, set the step size between windows as 24, to only produce one forecast per day."
    ]

neuralforecast/models/hint.py

@@ -134,7 +134,7 @@ class HINT:
 
     Args:
         h (int): Forecast horizon.
-        model (NeuralForecast model): Instantiated model class from [architecture collection](./models).
+        model (NeuralForecast model): Instantiated model class from [architecture collection](./models.html).
         S (np.ndarray): Dumming matrix of size (`base`, `bottom`) see HierarchicalForecast's [aggregate method](../hierarchicalforecast/utils.html#aggregate).
         reconciliation (str): HINT's reconciliation method from ['BottomUp', 'MinTraceOLS', 'MinTraceWLS'].
         alias (str, optional): Custom name of the model.
@@ -206,7 +206,7 @@ def fit(
         cross-learning the hierachy's series scales.
 
         Args:
-            dataset (TimeSeriesDataset): NeuralForecast's `TimeSeriesDataset` see details [here](./tsdataset)
+            dataset (TimeSeriesDataset): NeuralForecast's `TimeSeriesDataset` see details [here](./tsdataset.html)
             val_size (int): size of the validation set, (default 0).
             test_size (int): size of the test set, (default 0).
             random_seed (int): random seed for the prediction.
@@ -236,7 +236,7 @@ def predict(self, dataset, step_size=1, random_seed=None, **data_module_kwargs):
         bootstrapped sample reconciliation.
 
         Args:
-            dataset (TimeSeriesDataset): NeuralForecast's `TimeSeriesDataset` see details [here](./tsdataset)
+            dataset (TimeSeriesDataset): NeuralForecast's `TimeSeriesDataset` see details [here](./tsdataset.html)
             step_size (int): steps between sequential predictions, (default 1).
             random_seed (int): random seed for the prediction.
             **data_kwarg: additional parameters for the dataset module.

neuralforecast/models/timemixer.py

@@ -252,6 +252,7 @@ def forward(self, x_list):
 
 class TimeMixer(BaseModel):
     """TimeMixer
+
     Args:
         h (int): Forecast horizon.
         input_size (int): autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].