update desc e syllabus

general-infos/tsf_description_business.Rmd

@@ -34,14 +34,11 @@ with new methods.
 
 ## Course Structure
 
-The aim of the course is to teach the students how time series 
-forecasting problems can be solved in practice.  
-The state-of-the-art techniques are presented from a very practical 
-point of view, throughout R tutorials on each main topic. 
+The aim of the course is to teach how time series forecasting problems can be 
+solved in practice. The state-of-the-art techniques are presented from a very 
+practical point of view, throughout R tutorials on each main topic. 
 Python algorithms are also presented and used within R by means 
 of the *reticulate* package.
-Theoretical concepts are left to those who are interested in and 
-bibliographic references are listed at the end of the course.
 
 
 ## Contents
@@ -58,7 +55,7 @@ bibliographic references are listed at the end of the course.
 * Nested (Iterative) Forecasting
 * Global Modelling
 
-Specific business needs may be discussed during the lectures.  
+Specific business needs and adjustments may be discussed.  
 
 
 ## Duration
@@ -73,7 +70,3 @@ statisticians, IT specialists, developers, project managers and
 business leaders who want to develop the most in-demand skills to  
 solve time series forecasting problems.   
 
-
-## Requirements
-
-Basic statistic and programming knowledge.

general-infos/tsf_description_business_ita.Rmd

@@ -12,8 +12,6 @@ output:
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-  
-
 ## Introduzione
 
 Negli ultimi 15 anni, le richieste aziendali legate alla previsione di
@@ -37,10 +35,10 @@ tecnologie e algoritmi.
 ## Obiettivi
 
 L'obiettivo del corso è apprendere come i problemi di forecasting
-con dati temporali possono essere risolti in pratica.  
+con dati temporali possono essere risolti in pratica.
 I metodi e le tecnologie passate e attuali sono presentati da un
 punto di vista estremamente pratico attraverso la programmazione R.
-Anche numerosi algoritmi Python sono presentati e utilizzati.  
+Anche numerosi algoritmi Python sono presentati e utilizzati attraverso R.  
 
 
 ## Contenuti
@@ -75,8 +73,3 @@ vogliono acquisire know how sui metodi e sulle applicazioni riguardanti
 il forecasting di dati temporali e la tecnologie più in voga per risolvere 
 tali problematiche aziendali.  
 
-
-## Requisiti
-
-Sono richieste conoscenze di base della statistica e della programmazione.  
-

general-infos/tsf_syllabus.Rmd

@@ -1,8 +1,5 @@
 ---
-title: | 
-  | Time Series Forecasting:
-  | Machine Learning and Deep Learning with R and Python
-  | - Course Syllabus -
+title: "Time Series Forecasting  \n  with  \n  Machine Learning & Deep Learning  \n  - Course Program -"
 author: "Marco Zanotti"
 date: ""
 output:
@@ -91,6 +88,7 @@ knitr::opts_chunk$set(echo = TRUE)
 - TBATS  
 - STLM (Decomposition models)  
 - Facebook's Prophet  
+- Facebook's Neural Prophet  
 
 
 ## Lecture 5: Machine Learning Models
@@ -177,6 +175,8 @@ knitr::opts_chunk$set(echo = TRUE)
 **Forecasting Methods**:  
 - Nested Forecasting  
 - Global Modelling  
+
+
 <!--- Hierarchical Time Series Forecasting-->
 
 

general-infos/tsf_syllabus.html

@@ -8,7 +8,7 @@
         <meta name="author" content="Marco Zanotti" />
 
 
-    <title>tsf_syllabus.knit</title>
+    <title>Time Series Forecasting with Machine Learning &amp; Deep Learning - Course Program -</title>
 
         <script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
 // be compatible with the behavior of Pandoc < 2.8).
@@ -1522,10 +1522,10 @@
 
 
 
-      <h1 class="title"><div class="line-block">Time Series
-Forecasting:<br />
-Machine Learning and Deep Learning with R and Python<br />
-- Course Syllabus -</div></h1>
+      <h1 class="title">Time Series Forecasting<br />
+with<br />
+Machine Learning &amp; Deep Learning<br />
+- Course Program -</h1>
 
       <p class="authors">
            <span class="glyphicon glyphicon-user"></span> Marco Zanotti
@@ -1604,7 +1604,8 @@ <h2>Lecture 4: Time Series Models</h2>
 - Exponential Smoothing<br />
 - TBATS<br />
 - STLM (Decomposition models)<br />
-- Facebook’s Prophet</p>
+- Facebook’s Prophet<br />
+- Facebook’s Neural Prophet</p>
 </div>
 <div id="lecture-5-machine-learning-models" class="section level2">
 <h2>Lecture 5: Machine Learning Models</h2>
@@ -1679,8 +1680,8 @@ <h2>Lecture 11: Recursive Machine Learning Forecasting</h2>
 <h2>Lecture 12: Panel Data Forecasting</h2>
 <p><strong>Forecasting Methods</strong>:<br />
 - Nested Forecasting<br />
-- Global Modelling<br />
-<!--- Hierarchical Time Series Forecasting--></p>
+- Global Modelling</p>
+<!--- Hierarchical Time Series Forecasting-->
 <!--
 ### Lecture 13: Spark Backend
 

general-infos/tsf_syllabus_ita.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "Time Series Forecasting  \n  with  \n  Machine Learning & Deep Learning  \n  - Programma del Corso -"
+title: "Time Series Forecasting  \n  con  \n  Machine Learning & Deep Learning  \n  - Programma del Corso -"
 author: "Marco Zanotti"
 date: ""
 output:
@@ -86,6 +86,7 @@ knitr::opts_chunk$set(echo = TRUE)
 - TBATS  
 - STLM (Decomposition models)  
 - Facebook's Prophet  
+- Facebook's Neural Prophet  
 
 
 ## Machine Learning Models