# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 7 Conclusions  
The purpose of this present study was to forecast stock prices. The stock-price performance for the coming week was forecasted using historical data from Mobily, STC, and Zain. The first univariate analysis only applied the closing-price data as a single input directly in the LSTMDL projection model while the second multivariate analysis applied the closing, opening, highest, and lowest price data parallelly in the LSTMDL model to forecast the closing prices for the following seven days. The multivariate method predicted the closing prices for the next seven days more accurately than the univariate method, thereby making investments extremely profitable and secure. Other economic factors, such as governmental regulations, currency exchange rates, interest rates, inflation, Twitter sentiment, or hybrid methods, that influence financial markets may be used to build a knowledge base or as data for forecasting models. Short-term expectations are another variable that could, potentially, affect trend predictions or pricing. This present study only forecasted prices for the following seven days. Therefore, future studies may examine longer periods, such as 10 or 15 days. Long-term predictions may also be examined for further indepth analysis or guidance. This may include regulatory stability, reviews of quarterly stock performance, sales returns, and dividend returns, among other elements. Although longer time frames can be investigated and validated if a longer-term outlook is necessary, shorter time frames, such as the 10 day or 50 day simple moving average (SMA) or exponential moving average (EMA), can also be used. It is well known that a wide range of aspects, such as governmental regulations, corporate practices, and interest rates to name a few, impact stock prices. For that matter, any announcements regarding these factors impact stock prices. Even the best ML or DL models will be significantly impacted by natural catastrophes and other unforeseen events. Therefore, a hybrid method that considers all future elements, such as sentiment, news, and technical indicators, can be developed to yield a more vigorous and precise forecasting mechanism. The limitations we faced were not using more elements that affect stock price fluctuations, such as the resignation of the board chairman and transforming this news into time series and using them alongside current inputs. This is the challenge and upcoming research for us. Conceptualization, M.J.; Methodology, M.J. and M.D. ; Software, M.J.; Validation, M.J. and M.D. ; Formal analysis, M.J. and M.D. ; Investigation, M.J. and M.D. ; Resources, M.J.; Writing--original draft, M.J.; Writing--review & editing, M.J. and M.D. ; Visualization, M.J.; Funding acquisition, M.J. and M.D. All authors have read and agreed to the published version of the manuscript. This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant No. G: 663-611-1443. Project No. 258747. The authors, therefore, acknowledge with thanks DSR for technical and financial support. Not applicable. Figure 18: PAC Zain. **Data Availability Statement:** The dataset employed in this research can be retrieved online from: [[https://www.investing.com/indices/ttisi](https://www.investing.com/indices/ttisi)]([https://www.investing.com/indices/ttisi](https://www.investing.com/indices/ttisi)) (accessed on 20 March 2023). **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**  
The below table summarizes the details of the literary survey.