Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed Rider-based monarch butterfly optimization (Rider-MBO) algorithm. The proposed Rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data. The proposed stock market prediction model acquired a minimal MSE and RMSE of 7.2487 and 2.6923 that shows the effectiveness of the proposed method in stock market prediction.
Page Ranking holds great importance in any information retrieval system. We are well aware of the fact that the World Wide Web boasts a vast array of pages. It becomes the duty of search engines to provide the most relevant web pages to the user. The PageRank is one approach to rank web pages. However, it lays more stress on link structure of a page. Hence, more parameters need to be accommodated in the already suggested algorithm. This will only make it more efficient. In this paper, a time-based approach is proposed as an extension to PageRank and is defined incrementally.
Bank NIFTY index prediction is a challenging problem, which dictates that the market is highly stochastic, and there are temporally dependent predictions from chaotic data. Thus, the development of an effective prediction model is required as the basic necessity and in this paper, the Bank NIFTY index prediction system is developed using the Deep Convolutional Long Short-Term Memory (Deep-ConvLSTM) model that effectively predicts the Bank NIFTY index. The overall procedure of the proposed approach involves three steps. The initial step is feature extraction, the second step is clustering, and the tertiary step is the prediction. The input data is fed to the feature extraction step. Here, the feature extraction is performed based on the technical indicators, and then the clustering is done based on modified Sparse Fuzzy [Formula: see text]-Means (FCM) in order to find the effective features. Finally, the prediction is carried out based on Deep-ConvLSTM model, which is trained optimally using the proposed Adaptive-Rider-Monarch Butterfly Optimization (Adaptive-Rider-MBO) for performing accurate prediction. The performance of the Bank NIFTY index prediction based on Adaptive-Rider-MBO is evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed method achieves the minimal MSE of 2.010 and minimal RMSE of 1.418 based on the NIFTY Midcap 100 index.
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