# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 1 Introduction  
The study of deep learning models has been a long history in the last decades, and has become increasingly increasingly important in the field of computer science. In this field, deep learning models have been widely used in the field of computer science, such as machine learning, machine learning, and machine learning. In this field, deep learning models have been used to predict the economic information of the economicthe observations made up to this point [6; 7]. In training deep networks using sequential data, vanishing gradients may manifest. In response, LSTM networks, derivatives of RNNs, were devised [8]. Time-series data can be enhanced with the temporal feature extraction capabilities of CNNs, while long-range correlations between time steps can be discovered with transformer networks. One can use either of these methods to improve their time-series data. However, while most people agree that data attributes should guide model selection for time-lapse forecasting in deep learning, there are exceptions; the extensive evaluation of various models has sparked debates regarding the most suitable architecture or model for such data [9]. Although transformer models have shown great success in natural language processing (NLP) and other domains, they are not always the best choice for predicting stock exchange closing prices. Stock price data are time-series based, meaning they have a natural sequential structure, where past values strongly influence future ones [10; 11]. Traditional models like ARIMA, LSTM, or GRU are explicitly designed to handle this sequential dependence. While transformers can process sequences, their parallelized approach could be more intuitive for handling long-term temporal dependencies than models specifically designed for time-series data. Further, market volatility makes stock price data noisy and often unpredictable. With their complex architecture and large number of parameters, transformers may be more prone to overfitting on these noisy data, especially if the dataset is not large or diverse enough [12; 13]. It is crucial that time series exhibit periodicity. Seasonal decomposition and ARIMA are two techniques that may prove beneficial when the time series demonstrates a clear and uninterrupted periodic pattern. Complex models, such as those comprising multiple deep networks, may improve performance when the time series displays irregular patterns or cycles. Aspects that include the complexity, accessibility, and necessary interoperability of the data may affect the final determination [14]. Consequently, the model should align with the research's data and objectives. The fundamental goal of this endeavor is to improve the accuracy of financial time-series forecasts by applying deep learning models. Finding out which deep learning model excels in predicting financial time-series observations is the driving force for this study. This study will employ a multivariate hybrid deep learning model capable of handling multivariable input and predicting the close price of the stock index. Unlike univariate models, multivariate models yield higher accuracy predictions for dynamic systems that comprise multiple interconnected variables, such as financial systems [15] and capital market systems [16]. This is the primary rationale for selecting this methodology for the present study. Furthermore, predictions will be made simultaneously on all variables for the deep learning models that will be assessed. One potential approach to improve the prediction accuracy is to employ a trained deep learning network that simultaneously generates predictions and records the relationship between variables [17]. A \"multivariate\" approach integrates the above-mentioned strategies into the deep learning model. Forecast accuracy may be affected by both the features of time-lapse data and the design of a deep neural network model; this is another research objective.