Merge branch 'main' into polars-support

nbs/examples/PredictInsample.ipynb

@@ -228,11 +228,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../imgs_indx/predict_insample.png\" width=\"1000\" />"
+    "![](../imgs_indx/predict_insample.png)"
    ]
   },
   {
@@ -461,5 +460,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

nbs/losses.numpy.ipynb

@@ -21,13 +21,23 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# NumPy Evaluation\n",
     "\n",
-    "> NeuralForecast contains a collection NumPy loss functions aimed to be used during the models' evaluation. The most important train signal is the forecast error, which is the difference between the observed value $y_{\\tau}$ and the prediction $\\hat{y}_{\\tau}$, at time $y_{\\tau}$:$$e_{\\tau} = y_{\\tau}-\\hat{y}_{\\tau} \\qquad \\qquad \\tau \\in \\{t+1,\\dots,t+H \\}$$ The train loss summarizes the forecast errors in different evaluation metrics.<br><br>"
+    "> NeuralForecast contains a collection NumPy loss functions aimed to be used during the models' evaluation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most important train signal is the forecast error, which is the difference between the observed value $y_{\\tau}$ and the prediction $\\hat{y}_{\\tau}$, at time $y_{\\tau}$:\n",
+    "\n",
+    "$$e_{\\tau} = y_{\\tau}-\\hat{y}_{\\tau} \\qquad \\qquad \\tau \\in \\{t+1,\\dots,t+H \\}$$\n",
+    "\n",
+    "The train loss summarizes the forecast errors in different evaluation metrics."
    ]
   },
   {

nbs/losses.pytorch.ipynb

@@ -23,14 +23,27 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "12fa25a4",
+   "id": "fd532cb1-d11d-468e-a0e5-eb1101ba6662",
    "metadata": {},
    "source": [
     "# PyTorch Losses\n",
     "\n",
-    "> NeuralForecast contains a collection PyTorch Loss classes aimed to be used during the models' optimization. The most important train signal is the forecast error, which is the difference between the observed value $y_{\\tau}$ and the prediction $\\hat{y}_{\\tau}$, at time $y_{\\tau}$:$$e_{\\tau} = y_{\\tau}-\\hat{y}_{\\tau} \\qquad \\qquad \\tau \\in \\{t+1,\\dots,t+H \\}$$ The train loss summarizes the forecast errors in different train optimization objectives.<br><br>All the losses are `torch.nn.modules` which helps to automatically moved them across CPU/GPU/TPU devices with Pytorch Lightning. "
+    "> NeuralForecast contains a collection PyTorch Loss classes aimed to be used during the models' optimization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "096cfbec-1d59-454a-b572-5890103b2f1f",
+   "metadata": {},
+   "source": [
+    "The most important train signal is the forecast error, which is the difference between the observed value $y_{\\tau}$ and the prediction $\\hat{y}_{\\tau}$, at time $y_{\\tau}$:\n",
+    "\n",
+    "$$e_{\\tau} = y_{\\tau}-\\hat{y}_{\\tau} \\qquad \\qquad \\tau \\in \\{t+1,\\dots,t+H \\}$$\n",
+    "\n",
+    "The train loss summarizes the forecast errors in different train optimization objectives.\n",
+    "\n",
+    "All the losses are `torch.nn.modules` which helps to automatically moved them across CPU/GPU/TPU devices with Pytorch Lightning. "
    ]
   },
   {