# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 1 Literature Review  
This study will discuss a substantial issue related to the research topic. The problem is examining the methodology employed to forecast the economy's condition. The primary challenge in time-series forecasting is to employ practical approaches that align with the characteristics of the data. Employing conventional statistical methods like regression analysis and ARIMA models may pose challenges because of the non-linear characteristics of financial data. Given the possibility of inaccurate results, it may be unwise to depend extensively on these approaches for forecasting the economy's future performance. Numerous scholars have dedicated significant time and effort over the past several years to employing sophisticated methods to predict the stock markets of foreign countries. Global stock exchange market datasets are the basis for numerous academic research endeavors.  
Several advanced deep learning architectures and models have been developed in recent years as feasible solutions for forecasting time-series data and other applications. These models and architectures have been produced [18; 19]. A frequently chosen architecture for deep learning applications that involve predicting time-series data is the Convolutional RNN and Convolutional LSTM systems. According to studies by [20], using LSTM to forecast the flow of financial data produces highly accurate results. The CNN is an important deep learning architecture that, along with the LSTM, can predict the future development of time-series data [21]. The main impetus behind the development of CNN was to enhance the efficiency of data feature extraction to facilitate classification [22]. In contrast, several studies have shown that CNNs can effectively make precise predictions on time-series data [23].  
Several other deep learning models, such as ConvRNN [24], ConvLSTM [25], ConvGRU [26], Bi-directional ConvRNN [27], Bi-directional ConvLSTM [28], and Bi-directional ConvGRU [29] integrate LSTM and CNN characteristics. LSTM is designed to store data motion patterns, and CNN is designed to extract features. Integrating the two models is to take advantage of each model's qualities. As a consequence of this integration, the precision of time-series forecasts will subsequently improve. This study aligns with previous research carried out in the field of time-series prediction and uses the integrated deep learning approach. Ref. [30] also confirmed the effectiveness of this strategy in predicting air quality. Furthermore, previous studies have indicated that CNN and LSTM can accurately predict the onset of COVID-19 [31]. The researchers at [32] conducted a study to validate the effectiveness of various models for complex wind speed forecasting and specifically explored using mixed deep learning algorithms applied to wind speed data. A further study that used electrical load forecasting data [33] demonstrated similar findings, as it evaluated and empirically analyzed the most efficient deep learning models suitable for predicting short-term load.  
This article evaluates six prominent deep learning models: the Convolutional RNN, Convolutional LSTM, Convolutional GRU, Bi-directional ConvRNN, Bi-directional ConvLSTM, and Bi-directional ConvGRU. These models can predict time series and may be applied to datasets with multiple variables. References to earlier research and the characteristics of the models served as inspiration for this choice. Figure 1 presents a comparative examination of the six deep learning models' input/output flow topologies.