# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 1 Introduction  
There has been a significant increase in the use of deep-learning (DL) models to conduct multivariate time-series (TS) data analysis in many key application areas, such as banking, healthcare, environment, and other areas that benefit society, as well as crucial infrastructures that are powered by the internet of things or the climate [1]. Quantitative system-dynamics data from historical performance and qualitative fundamental data from various news sources affect the performance of TS data obtained from economic markets [2]. As both types of data can be combined to effectively identify patterns in economic data, they are currently of significant interest and are researched by both professionals and academics alike. Most extant studies have used data that has been derived from news sources to estimate the direction of stock movements. However, this caused a classification problem, which was the exact price of an asset in the future, posing a regression problem. Therefore, this present study hypothesised that using multiple datasets obtained from the Saudi Stock Exchange (Tadawul), specifically opening, lowest, highest, and closing prices, to conduct a multivariate analysis increases the accuracy of forecasting the future price of a stock [3]. As stock-market predictions heavily rely on a combination of mental vs. physical characteristics and rational vs. illogical behaviours to yield stable values, they are extremelydifficult to anticipate precisely. As such, it is one of the most significant topics in the computer-science industry at present. Stock-market prices for the next 10 days can be predicted by analysing a financial timeline over an extended period of time. Extant studies indicate that significantly accurate stock-price forecasts can be made using accurate coding. Nevertheless, some studies suggest that market hypotheses and predictions are merely hypothetical and inaccurate. Multiple models have been proposed to forecast stock prices. The variables chosen, the analysis method, and the modelling process significantly affect the forecasting accuracy of a model. Deep learning (DL) and machine learning (ML) have been frequently used to analyse and predict stock prices. Artificial intelligence (AI), a field of study in computer science and technology, has made considerable strides in recent years and significantly simplified multiple real-world problems [4; 5]. As there are multiple variables that affect a prediction, it is simpler to project the weather or a certain trait than the stock market, as it is in a constant state of flux. More specifically, stock prices fluctuate independently at times since financial markets have regular ups and downs. Therefore, compiling accurate historical data is no small feat as the stock market is volatile. Moreover, a variety of factors, such as economic, business, personal, political, and social issues, could affect the market [6; 7]. As such, it is virtually impossible to predict changes in stock prices due to the stock market's extreme unpredictability. Globalisation, as well as the development of information and communication technology, (ICT) has increased the number of individuals seeking to make exorbitant returns from the stock market. As such, stock-market predictions have become crucial for investors. The fundamental method and the technical method are the two different methods of forecasting the stock market [8]. Unlike the technical method, which uses analyses graphs to forecast future stock prices, the fundamental method examines every facet of a firm's internal worth. However, the fundamental method is not without drawbacks as it generates fewer time predictions and lacks compliance. As such, many stock-market prediction studies utilise the technical method. Moving averages, genetic algorithms, easy measurement, regression, and support-vector machines (SVMs) are some of the ML methods that have been employed to address this issue. However, none of them produce predictions that are extremely precise.