DOC: Tutorial on cross-validation

nbs/docs/tutorials/01_getting_started_complete.ipynb

@@ -22,7 +22,7 @@
     "\n",
     "Follow this article for a step to step guide on building a production-ready forecasting pipeline for multiple time series. \n",
     "\n",
-    "During this guide you will gain familiary with the core `NueralForecast`class and some relevant methods like `NeuralForecast.fit`, `NeuralForecast.predict`, and `StatsForecast.cross_validation.`\n",
+    "During this guide you will gain familiarity with the core `NeuralForecast`class and some relevant methods like `NeuralForecast.fit`, `NeuralForecast.predict`, and `StatsForecast.cross_validation.`\n",
     "\n",
     "We will use a classical benchmarking dataset from the M4 competition. The dataset includes time series from different domains like finance, economy and sales. In this example, we will use a subset of the Hourly dataset. \n",
     "\n",

nbs/mint.json

@@ -39,6 +39,7 @@
         "docs/capabilities/overview.html",
         "docs/capabilities/objectives.html",
         "docs/capabilities/exogenous_variables.html",
+        "docs/capabilities/cross_validation.html",
         "docs/capabilities/hyperparameter_tuning.html",
         "docs/capabilities/predictinsample.html",
         "docs/capabilities/save_load_models.html",
@@ -120,6 +121,7 @@
             "models.nhits.html",
             "models.nlinear.html",
             "models.patchtst.html",
+            "models.rmok.html",
             "models.rnn.html",
             "models.softs.html",
             "models.stemgnn.html",

nbs/sidebar.yml

@@ -40,6 +40,7 @@ website:
           - models.nhits.ipynb
           - models.nlinear.ipynb
           - models.patchtst.ipynb
+          - models.rmok.ipynb
           - models.rnn.ipynb
           - models.softs.ipynb
           - models.stemgnn.ipynb