# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 2 Literary Survey  
It is essential to review extant studies prior to conducting research. The experimental findings of [PERSON] and [PERSON] (2020) demonstrated that the proposed hybrid framework, which could be used for fiscal data analysis and research or stock-market monitoring, had the best projection accuracy. As the primary component of the mixed framework, a dropout strategy and particle swarm optimisation (PSO) were used to augment a DL network predictor that was based on an LSTM network. The proposed framework for projecting stock closing prices outperformed conventional models in terms of prediction. Theoretically, the LSTM network is particularly well suited for financial TS projection due to its cyclic nature, which gives it the function of long-term memory. The deep hybrid framework's components included data processing, a DL predictor, and the predictor optimisation technique [13]. [PERSON] et al. (2020) used bagging, decision tree, random forest, gradient boosting, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), RNN, artificial neural networks (ANN), and LSTM. Different ML techniques were used to forecast future stock-market group values. From the Tehran Stock Exchange, four groups, i.e., petroleum, diversified financiala, nonmetallic minerals, and basic metals, were selected for experimental assessments. Due to its intrinsic subtleties, nonlinearity, and complexity, the estimation of stock group prices has long been appealing to and challenging for investors [14]. [PERSON] et al. (2022) used a hybrid prediction rule ensembles (PRE) and deep neural networks (DNN) stock-prediction model to address nonlinearity in the examined data. The mean absolute error (MAE) and root-mean-square-error (RMSE) metrics were used to calculate the results of the hybrid PRE-DNN stock estimation model. The stock-prediction rules with the lowest RMSE score were selected from all the stock-prediction rules generated using the PRE approach. As it improved the RMSE by 5% to 7%, the hybrid PRE-DNN stock-estimation model outperformed the DNN and ANN single prediction models. Due to the many aspects which affect the stock market, including corporate earnings, geopolitical tension, and commodity prices, stock prices are prone to volatility [15]. The single-layer RNN model suggested by [PERSON] et al. (2023) improved prediction accuracy by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental findings supported the efficacy of the proposed prediction methodology, which helps investors increase their earnings by selecting wisely. The model uses the input stock data to anticipate the closingprice and highest price of a stock for the following day. The obtained results demonstrated that CNN performed the worst while the LSTM outperformed the CNN-LSTM, the CNN-RNN outperformed the CNN-LSTM and the LSTM, and the proposed single-layer RNN model outperformed all of them. Due to the nonlinearity, substantial noise, and volatility of TS data on stock prices, stock-price estimation is highly difficult ([PERSON] et al. (2022). [PERSON] et al. (2022) proposed methods of predicting the oil prices of Brent Crude and West Texas Intermediate (WTI) using multivariate TS of key S&P 500 stock prices, Gaussian process modelling, vine copula regression, DL, and a smaller number of significant covariates. The 74 large-cap key S&P 500 stock prices and the monthly log returns of the oil prices of both companies for the timespan of February 2001 to October 2019 were used to test the proposed methods on the actual data. With regards to the extent of prediction errors, the vine copula regression with NLPCA was generally superior to the other proposed approaches. To reduce the dimensions of the data, a Bayesian variable selection and nonlinear principal component analysis (NLPCA) were used ([PERSON] et al. (2022)) used a unique locally adaptable and interpretable DL architecture supplemented with RNNs to render model explainability and superior predictive accuracy for time-series data.