Stock market is a significant element of economic market. Accurate forecasting of stock market is very helpful to shareholders because future prediction of a stock value will elevate the profits of investors. The data acquired from the stock market is a time-series data, in which the values of the stock prices are inherently varied with respect to time. Due to its complexity nature and nonlinearity characteristics, the prediction of stock market becomes very difficult and still it remains a challenging task. In order to cope up with such limitation, this research proposes an effective strategy called Deep Recurrent Rider LSTM to provide an accurate detection of stock market values. The accurate forecasting of stock market is carried out with two classifiers, namely Rider Deep Long Short-Term Memory (Rider Deep LSTM) and Deep Recurrent Neural Network (Deep RNN). The Rider Deep LSTM is derived by the integration of Rider concept with Deep LSTM, whereas the Deep RNN is trained using the proposed Shuffled Crow Search Optimization (SCSO). Moreover, the SCSO is derived by the integration of Shuffled Shepherd Optimization (SSO) algorithm and Crow Search Algorithm (CSA). Finally, the predicted output is determined based on the error condition. Furthermore, the proposed Deep Recurrent Rider LSTM achieved the MSE and RMSE of 0.018 and 0.132 that shows higher performance with better accuracy. The stock market prediction using the proposed classification model is accurate and improves the effectiveness of the classification.
Prediction using ML models is not well adapted in many portions of business decision-making due to a lack of clarity and flexibility. In order to provide a positive risk-adjusted price for stocks by evaluating historical transaction data and retaining more accuracy with a reduced error rate, the suggested framework aims to use deep learning method. The deep learning methodology, which can handle time-series data, is applied in this work. The measurements of MSE and RMSE error rates, which indicate how far the measured values are from the regression line, are used to produce the findings. The dispersion of these residuals is evaluated by RMSE. It demonstrates how densely the data is clustered around the line of best fit. In this work, a novel deep learning approach is compared to deep LSTM, GA, and Harris Hawk optimization. Outcomes were obtained and exhibited for the various firm stocks dataset as part of this investigation, which amply demonstrates the usefulness of the proposed strategy with a lower error rate.
Due to a lack of clarity and flexibility, prediction leveraging ML models is not well fitted in many sections of commercial decision processes. Proposed model aim to employ deep learning strategy in the stock market pricing area to generate positive risk-adjusted price by analyzing previous transaction data and maintaining greater accuracy with a lower error rate. In this study, the deep learning approach is used, which is capable of handling time-series data. The results are obtained with evaluation of error rate metric MSE & RMSE which express how distant the data points are from the regression line. RMSE measures the dispersion of these residuals. It shows how concentrated the data is on the best fit line. This study compares a unique deep learning methodology with deep LSTM, GA and Harris Hawk optimization. As a part of this analysis results are observed and plotted for the various company stocks dataset, which clearly shows the effectiveness of proposed approach with reduced error rate.
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