## 2 Literature Review  
As a type of recurrent neural network (RNN), LSTMs are capable of learning long-term interdependencies in data. This makes them an appropriate technique for processing and forecasting significant scenarios with comparatively extended intervals and delays in time series. In this section of the study, the research work related to the advancement of LSTM and its implementation for stock market prediction is discussed. It is witnessed from the previous studies that RNN is a widely used technique to forecast the indexes [15; 16; 17]. However, this technique suffers from the vanishing gradient problem, which makes the prediction results misleading. An LSTM network can be used to solve the problem of vanishing gradients since it replaces the hidden layer units of RNNs with memory cells, making it more suitable for stock price prediction [18]. Several studies have demonstrated that the LSTM network produces better predictions than any other neural network. Based on this characteristic, LSTM turns out to be a significant tool in natural language recognition [19; 20; 21], time series prediction [22; 23; 24], specifically stock price prediction [25; 26; 27; 28], as well as other areas such as water desalination [29; 30; 31], material sciences [32], welding of materials [33], laser technology [34; 35], metal cutting [36], and material processing [37]. To forecast the KOSPI 200 index, a novel hybrid model that integrates LSTM and a type of GARCH model was proposed by [38]. According to this study, the proposed hybrid model outperforms the other models in terms of minimum values of several statistical metrics, including MAE, mean squared error (MSE), heteroscedasticity-adjusted MAE (HMAE), and heteroscedasticity-adjusted MSE (HMSE). The decomposition-and-ensemble framework is a well-known hybrid method that works on the principle of \"divide-and-conquer\" [39; 40]. The core idea of such hybrid models for the purpose of forecasting complex financial time series is to divide the actual data into small components, technically known as IMFs, and a monotone residue. Now it is quite simple for the model to predict this subseries correspondingly. Furthermore, the final prediction results of the actual series can be obtained by combining the forecasted values obtained from ensemble modeling of the different independent IMFs. In particular, a hybrid attention-based EMD-LSTM model to forecast the indexes was proposed by [41]. The LSTM forecasting model that works on the mechanism of attention is capable for obtaining the association between the forecasted values and input attributes, to reduce the prediction error. With the help of regression analysis, the value of the coefficient of correlation is nearly equal to one, suggesting a better prediction performance from the suggested EMD-LSTM-ATTE technique. The EMD-LSTM-ATTE model prediction performance is better than the competing forecasting models, according to numerical values of statistical metrics such as MAE, RMSE, and MAPE. The model proposed by [42] for forecasting the stock market utilizes stockholders' emotional tendencies to build a deep learning algorithm. Firstly, the investor's sentiment is taken into consideration, which can successfully enhance the prediction performance of the model. Secondly, since the stock price data is very complex and nonlinear, therefore, its accurate prediction is often not only difficult but also challenging. Thirdly, the stacked LSTM model is implemented because of its use of examining associations amongst time-series data through its memory function. Based on the investigational results, it is evident that the revised stacked LSTM model provides the best prediction performance and reduces the time delay at the same time. To forecast stock values, the authors of [43] presented a hybrid deep learning neural network built on the CEEMD, transformer, LSTM, GRU, and high-frequency adaptive structure. The efficiency of the suggested model is checked on 100 stocks selected from CSI-300.