# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 1 Introduction
This has given rise to the use of mixed-method approaches. Although problem-solving methods have been around for a while, in the data-science industry, these are some of the most difficult issues to solve as many significant concerns are involved when predicting future prices and spotting trends in stock-market data. This present study examined a method that uses a long short-term memory (LSTM) or temporary memory algorithm. A potent technique for handling TS data is the recurrent neural network (RNN). In many ways, LSTMs are subordinate to sensory RNN networks and feed broadcasts [9]. This is due to the fact that, by choosing long patterns, they possess a commemorative quality. Few RNN models have been utilised to predict movements in the stock market. An LSTM is one of the most effective RNN structures. This present study discusses the distinctions between univariate and multivariate analysis as it primarily examines multivariate TS analysis. A TS that only contains one variable is referred to as a univariate TS. A TS data's value at time step 't' is basically derived from its preceding time steps, \(t-1\), \(t-2\), \(t-3\), and so on. Unlike multivariate models, univariate models are easier to construct. The closing price of a capital asset is typically the variable of interest when predicting stock prices [7]. Unlike a univariate TS, multivariate TSs do not contain single dependent variables but several interconnected variables [10]. It also considers numerous influencing aspects as well as noninfluencing aspects. Predicting stock-market prices using multivariate analysis depends on many different variables, such as opening, highest, lowest, and closing prices. Therefore, multivariate models are typically more challenging [11] to develop than univariate models. This poses difficulties, including the management of several parameters in a high dimensional space [12]. This present study concentrates on the short-term price behaviour of the communication industry, which is made up of three major firms: Etihad Etisalat (Mobily), Saudi Telecom Company (STC), and Zain KSA. These three businesses were chosen as they best reflect the economy of the Kingdom of Saudi Arabia (KSA) in the Saudi Stock Exchange (Tadawul) as they are heavily traded. The KSA's communication industry is among the largest and fastest emerging markets in North Africa and the Middle East, with Mobily, STC, and Zain KSA as the top players in the mobile market. In 2022, the combined market valuation of these three firms surpassed 58 billion Saudi riyals. The main contributions of this paper are as follows:  
1. Outlines what is required to conduct a time-series (TS) analysis, as well as demonstrates how each of the univariate and multivariate models examined is satisfied and applied to the proposed LSTM DL;
2. Provides an indepth comparison between univariate and multivariate time series (TS) to explain why a multivariate TS is better than a univariate TS;
3. Uses real datasets to depict the results of the experimental analysis, which indicated that other prices affect the predictive accuracy of the proposed LSTM DL. Section 1 provides an introduction to the researched topic while Section 2 is a Literary Survey that discusses relevant extant studies and how the present study differs from them. In Section 3, the dataset that this present study used is discussed. Section 4 outlines the methodology that was used to develop the proposed LSTM DL while Section 5, Experiment Results, that discusses the prediction algorithm of the proposed LSTM DL. And Section 6 contains the conclusion, which is discussed.