Update presentation.tex

presentation.tex

@@ -1,4 +1,4 @@
-\documentclass[compress,mathserif,fleqn,10pt]{beamer}
+\documentclass[compress, mathserif, fleqn, 10pt]{beamer}
 \useoutertheme{split}
 \useoutertheme[subsection=false]{smoothbars}
 \useinnertheme[shadow=true]{rounded}
@@ -15,10 +15,10 @@
 \def\beamer@writeslidentry@miniframesoff{%
 	\expandafter\beamer@ifempty\expandafter{\beamer@framestartpage}{}% does not happen normally
 	{%else
+
 		% removed \addtocontents commands
 		\clearpage\beamer@notesactions%
-	}
-}
+} }
 \newcommand*{\miniframeson}{\let\beamer@writeslidentry=\beamer@writeslidentry@miniframeson}
 \newcommand*{\miniframesoff}{\let\beamer@writeslidentry=\beamer@writeslidentry@miniframesoff}
 \makeatother
@@ -37,42 +37,53 @@
 \usepackage{tikz}
 
 \title{Transformers and Multi-features Time2Vec for Financial Prediction}
-\author[Bui Nguyen Kim Hai, Nguyen Duy Chien]{Bui Nguyen Kim Hai, Nguyen Duy Chien}
+\author{[}
+Bui
+Nguyen
+Kim
+Hai,
+Nguyen
+Duy
+Chien]{Bui Nguyen Kim Hai, Nguyen Duy Chien}
 %\institute{Department of Numerical Analysis, Faculty of Informatics\\ ELTE Eötvös Loránd University, Budapest, Hungary}
-\date{\scriptsize \emph{TDK CONFERENCE – IT SCIENCE SECTION, 2024 SPRING}\\\bigskip Budapest, Hungary\\ May 29, 2024}
+\date{\scriptsize \emph{TDK CONFERENCE – IT SCIENCE SECTION, 2024 SPRING}\\
+	\bigskip
+	Budapest, Hungary\\ May 29, 2024}
 
 \begin{document}
-
-	\abovedisplayskip=1pt
-	\belowdisplayskip=2pt
-	\abovedisplayshortskip=1pt
-	\belowdisplayshortskip=2pt
+	\abovedisplayskip=1pt \belowdisplayskip=2pt \abovedisplayshortskip=1pt \belowdisplayshortskip=2pt
 
 	\begin{frame}
 		\titlepage
 	\end{frame}
 
-	\begin{frame}\frametitle{Outline}
+	\begin{frame}
+		\frametitle{Outline}
 		\tableofcontents
 	\end{frame}
 
 	\section{Introduction}
-	\begin{frame}\frametitle{Outline}
+	\begin{frame}
+		\frametitle{Outline}
 		\tableofcontents[currentsection]
 	\end{frame}
 
 	\subsection{Motivation}
 	\begin{frame}{Motivation}
 		\begin{block}{By other works}
 			\begin{itemize}
-				\item Researchers try to combine Time2Vec with CNN, RNN, LSTM, and Attention mechanism
+				\item Researchers try to combine Time2Vec with CNN, RNN, LSTM, and
+				Attention mechanism
+
 				\item For instances:
 
-					\begin{itemize}
-						\item Aeroengine Risk Assessment
-						\item Predicting Production in Shale and Sandstone Gas Reservoirs
-						\item Stock Price Forecasting
-					\end{itemize}
+				\begin{itemize}
+					\item Aeroengine Risk Assessment
+
+					\item Predicting Production in Shale and Sandstone Gas Reservoirs
+
+					\item Stock Price Forecasting
+				\end{itemize}
 			\end{itemize}
 		\end{block}
 		\smallskip
@@ -85,7 +96,9 @@
 		\begin{block}{By observing trends}
 			\begin{itemize}
 				\item Stock's trend is a Markov process
+
 				\item Historical data offers limited foresight
+
 				\item Stocks having similar trend is more promising
 			\end{itemize}
 		\end{block}
@@ -94,7 +107,7 @@
 	\begin{frame}{Motivation: Cross-correlation to NASDAQ}
 		\centerline{\includegraphics[width=0.85\textwidth]{images/nas_base.eps}}
 	\end{frame}
-
+	
 	\begin{frame}{Motivation: Cross-correlation to Exxon Mobil}
 		\centerline{\includegraphics[width=0.85\textwidth]{images/exx_base.eps}}
 	\end{frame}
@@ -108,24 +121,26 @@
 			Handling temporal problems in sequential data and time-series analysis.
 		\end{block}
 		\begin{block}{LSTM}
-			Using gates, LSTM enables network to learn long-term dependencies and prevent the vanishing gradient problem.
+			Using gates, LSTM enables network to learn long-term dependencies and
+			prevent the vanishing gradient problem.
 		\end{block}
 		\begin{block}{Transformer}
-			The SOTA architecture that works well in many area such as NLP, and time-series 
+			The SOTA architecture that works well in many area such as NLP, and time-series
 		\end{block}
 		\begin{block}{Time2Vec}
 			Use to embed the time-series data to vector
 		\end{block}
 	\end{frame}
 
 	\section{Proposed model and techniques}
-	\begin{frame}\frametitle{Outline}
+	\begin{frame}
+		\frametitle{Outline}
 		\tableofcontents[currentsection]
 	\end{frame}
 
 	%\subsection{Behavioral similarity of stocks}
 	%\begin{frame}{Behavioral similarity of stocks}
-		
+
 	%\end{frame}
 
 	\subsection{Data collection}
@@ -142,6 +157,7 @@
 		\begin{block}{Collected datasets}
 			\begin{itemize}
 				\item \structure{Group1}: NASDAQ, S\&P500, DJI, DAX
+
 				\item \structure{Group2}: Exxon Mobil, Chervon
 			\end{itemize}
 		\end{block}
@@ -154,9 +170,13 @@
 		\begin{block}{Techniques}
 			\begin{itemize}
 				\item \structure{Fill-forward}: Filling missing data in dataset
+
 				\item \structure{Moving Average}: Smoothing dataset by averaging data
+
 				\item \structure{Percentage Change}: Compute the difference in the data
+
 				\item \structure{Min-Max Normalization}: Normalizing dataset
+
 				\item \structure{Geometry Mean Not NaN (GMNN)}: Combining multiple datasets
 			\end{itemize}
 		\end{block}
@@ -172,12 +192,12 @@
 					\begin{itemize}
 						\item \structure{Union}: Handling length difference when combining datasets
 						\smallskip
+
 						\item \structure{Invariant}: Keeping the data stays normalized
 						\smallskip
 
 						\item \structure{Representation}: The output reflects the whole datasets
 						\smallskip
-
 					\end{itemize}
 				\end{block}
 				\vspace*{1cm}
@@ -208,16 +228,23 @@
 				\begin{block}{Roles}
 					\begin{itemize}
 						\item \structure{Time2Vec}
-							\begin{itemize}
-								\item \structure{Linear}: Capturing linear trends \smallskip
-								\item \structure{Sine, Cosine}: Encoding positions and capturing periodic behaviors \smallskip
-								\item \structure{Concat}: Concatenating above three layers 
-							\end{itemize}
+						\begin{itemize}
+							\item \structure{Linear}: Capturing linear trends
+							\smallskip
+
+							\item \structure{Sine, Cosine}: Encoding positions and capturing
+							periodic behaviors
+							\smallskip
+
+							\item \structure{Concat}: Concatenating above three layers
+						\end{itemize}
 						\bigskip
+
 						\item \structure{Attention Layers}
-							\begin{itemize}
-								\item To study the trend from different aspects, positions \smallskip
-							\end{itemize}
+						\begin{itemize}
+							\item To study the trend from different aspects, positions
+							\smallskip
+						\end{itemize}
 					\end{itemize}
 				\end{block}
 			\end{column}
@@ -230,10 +257,15 @@
 				\begin{block}{Roles}
 					\begin{itemize}
 						\item \structure{Time2Vec}: Catch continuous attribute of time
+
 						\item \structure{Concat}: Apply Residual Connection
+
 						\item \structure{Attention}: Deep understanding trend movements
+
 						\item \structure{Pooling}: Reducing dimension
+
 						\item \structure{Dropout}: Prevent over-fitting
+
 						\item \structure{Dense}: Apply activation functions (ReLU)
 					\end{itemize}
 				\end{block}
@@ -254,7 +286,9 @@
 					\begin{minipage}[t][2cm][t]{\textwidth}
 						\begin{itemize}
 							\item De-normalized
+
 							\item De-percentage change
+
 							\item De-moving average
 						\end{itemize}
 					\end{minipage}
@@ -265,6 +299,7 @@
 					\begin{minipage}[t][2cm][t]{\textwidth}
 						\begin{itemize}
 							\item Output is \textbf{normalized} (Invariant)
+
 							\item Target is \textbf{one} dataset, output only reflects that one
 						\end{itemize}
 					\end{minipage}
@@ -292,56 +327,69 @@
 		\centerline{Comparing 6 metrics with respect to Exxon (Left), NASDAQ (Right)}
 	\end{frame}
 
-	\begin{frame}\frametitle{Conclusion}
+	\begin{frame}
+		\frametitle{Conclusion}
 		\begin{block}{Conclusion}
 			\smallskip
 			By leveraging multiple criteria to evaluate the proposed model such as
 			\begin{itemize}
 				\item MAE, MAPE, RMSE, MSE, R2-score (price prediction task)
 				\smallskip
+
 				\item Accuracy (trend forecasting task)
 			\end{itemize}
-			 \bigskip
-
-			 We can proudly say that, the multi-feature model
-			 \begin{itemize}
-			 	\item \textbf{Outperforms} the single-feature one in most cases and they are \textbf{extremely close} to each other in other scenarios.
-			 	\smallskip
-			 	\item Usually yields \textbf{better} result than the SOTA in almost every contexts.
-			 \end{itemize}
-			 \smallskip
+			\bigskip
+
+			We can proudly say that, the multi-feature model
+			\begin{itemize}
+				\item \textbf{Outperforms} the single-feature one in most cases and they
+				are \textbf{extremely close} to each other in other scenarios.
+				\smallskip
+
+				\item Usually yields \textbf{better} result than the SOTA in almost
+				every contexts.
+			\end{itemize}
+			\smallskip
 		\end{block}
 	\end{frame}
 
 	\section{Summary}
-	\begin{frame}\frametitle{Outline}
+	\begin{frame}
+		\frametitle{Outline}
 		\tableofcontents[currentsection]
 	\end{frame}
 
 	\begin{frame}{Summary}
 		\begin{exampleblock}{Summary}
 			\begin{itemize}
 				\item We explore deep learning for challenging stock price prediction
-				\item Paving the way for new feature studies and applications in various deep learning models
-				\item Demonstrates correlation-based features and innovative neural networks improve stock
-				price prediction
+
+				\item Paving the way for new feature studies and applications in various
+				deep learning models
+
+				\item Demonstrates correlation-based features and innovative neural networks
+				improve stock price prediction
 			\end{itemize}
 		\end{exampleblock}
 
 		\begin{block}{Further Research}
 			\begin{itemize}
 				\item Fine-tuning the architecture
+
 				\item Continuing improving processing methods
+
 				\item Comparing to other SOTA neural networks like KAN
+
 				\item Applying the architecture to other areas
 			\end{itemize}
 		\end{block}
 	\end{frame}
 	\miniframesoff
 	\section*{}
 	\begin{frame}
-		\begin{beamercolorbox}[sep=8pt,center,shadow=true,rounded=true]{title}
-			\usebeamerfont{title}Thank you for your attention!\par%     
+		\begin{beamercolorbox}
+			[sep=8pt,center,shadow=true,rounded=true]{title} \usebeamerfont{title}Thank
+			you for your attention!\par%
 		\end{beamercolorbox}
 	\end{frame}
 \end{document}