content summarized

Author: moonlanderr

samples/04_gis_analysts_data_scientists/forecasting_daily_power_consumption_using_deep_learning_timeseries.ipynb

@@ -17,23 +17,8 @@
     "* [Connecting to ArcGIS](#3)\n",
     "* [Accessing & Visualizing the datasets](#4) \n",
     "* [One-step univariate forecasting](#5) \n",
-    "    * [Data Preparation](#6)              \n",
-    "    * [Model Building](#8)\n",
-    "    * [Model Training ](#12) \n",
-    "    * [Power Consumption Forecast & Validation](#13)\n",
-    "    * [Result Visualization](#16)\n",
-    "* [Multi-Step Multivariate Forecasting](#17) \n",
-    "    * [Data Preparation](#18)               \n",
-    "    * [Model Building](#20)\n",
-    "    * [Model Training ](#24) \n",
-    "    * [Power Consumption Forecast & Validation](#25)\n",
-    "    * [Result Visualization](#28)\n",
+    "* [Multi-Step Multivariate Forecasting](#17)\n",
     "* [One-Step multivariate forecasting](#29) \n",
-    "    * [Data Preparation](#30)      \n",
-    "    * [Model Building](#33)\n",
-    "    * [Model Training ](#37) \n",
-    "    * [Power Consumption Forecast & Validation](#38)\n",
-    "    * [Result Visualization](#41)\n",
     "* [Conclusion](#42)\n",
     "* [Data resources](#43)"
    ]
@@ -4090,9 +4075,9 @@
  ],
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   "kernelspec": {
-   "display_name": "conda_arcgis_2.2_vm_1stMarch2024",
+   "display_name": "condapac_18March2024",
    "language": "python",
-   "name": "conda_arcgis_2.2_vm_1stmarch2024"
+   "name": "condapac_18march2024"
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@@ -4104,7 +4089,7 @@
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    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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