add some TODO's in notebook

tsforecast.ipynb

@@ -1425,6 +1425,9 @@
     "        method=\"nm\",\n",
     "        maxiter=100\n",
     ")\n",
+    "#TODO : Try out different values for m (seasonality) -- can influence comp. cost\n",
+    "#TODO : Perform feature engineering, e.g. rolling means\n",
+    "#TODO : Try out different history sizes\n",
     "\n",
     "# Forecast\n",
     "y_pred = autoarima_model.predict(n_periods=len(y_test))\n",
@@ -1470,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "9796045c",
    "metadata": {},
    "outputs": [
@@ -1503,6 +1506,9 @@
     "    hist_size=hist_size\n",
     ")\n",
     "\n",
+    "#TODO : Perform feature engineering, e.g. rolling means\n",
+    "#TODO : Perform feature selection and combine with e.g. PCA\n",
+    "#TODO : Try out different history sizes\n",
     "\n",
     "X_train_xgb = reshaping(X_train)\n",
     "X_test_xgb = reshaping(X_test)\n",
@@ -38817,7 +38823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "abeddbe8",
    "metadata": {},
    "outputs": [
@@ -39034,7 +39040,8 @@
     "model.add(Dense(1))\n",
     "model.compile(optimizer=\"adam\", loss=\"mse\")\n",
     "model.summary()\n",
-    "\n",
+    "#TODO : Try out different architectures, like Conv1D, Bidirectional LSTM, etc.\n",
+    "#TODO : Try out different history sizes\n",
     "\n",
     "# fit the model\n",
     "model.fit(X_train, y_train, epochs=50)\n",
@@ -40432,6 +40439,8 @@
     "\n",
     "# Initialize and fit the Prophet model\n",
     "prophet_model = prophet.Prophet()\n",
+    "#TODO : Add any additional Prophet parameters if needed\n",
+    "#TODO : Add extra regressors if needed\n",
     "prophet_model.fit(df_train)\n",
     "\n",
     "# Create future dataframe for prediction\n",
@@ -40481,8 +40490,7 @@
     "axes[1].legend()\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n"
+    "plt.show()\n"
    ]
   }
  ],