## 1 Introduction
Additionally, such techniques frequently demand numerous propositions and earlier understanding, such as what is the statistical distribution of the data, the justifiable range for numerous parameters, and their connections. Although, as a complicated structure with a good deal of effective attributes and an unpredictable situation, the stock market inclines to show robust attributes, which forms the classical systematic approaches unsuccessful. Furthermore, the volume of the data used by building the model and then for the prediction of the stocks are frequently enormous, increasing countless difficulties in training the model. With such characteristics, it becomes inefficient to forecast the stock market by implementing the well-known classical approaches. In the recent past, financial experts have been extensively applying machine learning techniques for prediction problems, especially support vector regression (SVR) [9], as well as artificial neural networks (ANNs) [10]. Moreover, the development of deep learning techniques became an emerging field for machine learning because of its outstanding capability to handle nonlinear time series without any input features to build its architecture. Such techniques have a strong data handling capacity that became helpful in solving the complexities triggered by such nonlinear financial time series. For this reason, the integration of deep learning and financial time series has a remarkably outstretched ability, but limited studies are available regarding the prediction of the stock market with the help of deep learning techniques [11; 12]. In this study, we forecast the stock market by integrating our proposed new version of EMD based on the Akima spline interpolation technique and LSTM network. The main strength of this research work for the prediction of financial time series is summarized in the following way. The decomposition method of EMD and its different versions are ordinary techniques that are extensively implemented in the field of engineering to process signals as well as financial time-series investigation and prediction [13], therefore, to extract the trend component from the stock price time-series data, it was suggested to implement the new version of EMD. The primary objective is to steadily break down the variations of the diverse composite time-series data to achieve a sequence of IMFs, and a unique monotone residue component [14]. Hence, the complex financial time series is decomposed, and then the prediction is made from the decomposed simple systems to obtain enhanced efficacy. Secondly, we suggest implementing a hybrid LSTM model that is developed by using all the subcomponents (residue) that are correlated with the actual stock closing prices, and finally, the prediction is made by aggregating individual forecasts obtained from each hybrid LSTM model. Though several researchers have practiced deep learning for financial time series prediction, many of them utilized it for classification problems, and considerably few scholars have used it for regression problems. Additionally, amongst various frequently used deep learning techniques, such as convolutional neural networks (CNN), deep belief networks, and LSTM, we selected the improved hybrid LSTM model with multiple hidden layers having the outstanding ability to examine the dependencies among time-series data at various time points through its memory function. Thus, an effort has been made to forecast the stock market with the help of implementing the aforementioned hybrid new version of EMD based on the Akima spline interpolation technique and LSTM network. Specifically, we integrated LSTM with Akima-EMD and suggested a stock market forecasting model. The characteristics of our proposed model are twofold, i.e., to improve the forecast accuracy as well as to minimize time delay. To sum up, a financial time series forecasting model that combines Akima-EMD and hybrid LSTM is suggested.