Abstract
A stock market has an extreme effect on today’s economy. It has always been a tough job to invest in stock market. In a world where inflation increases at 3% per year, we must invest our money to guarantee our future. The market does not allow us to predict the future of the assets with high accuracy. However, with the successful development in technologies, the opportunities to gain a fortune from the markets are increasing, and it also helps experts predict the stock market. The extensive aim of this paper is to use long short-term memory (LSTM)- based Recurrent Neural Networks (RNNs) to build a machine learning model that can monitor and predict the stock market prices. The performance of this model was determined by accuracy, Root Mean Squared Error (RMSE), and how epochs can modify our model. We evaluated the model using Yahoo Finance data and reported higher accuracy with a prediction of 88.2% in the same cohorts of stocks.
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Patil, S., Shankar, V.G., Devi, B., Singh, A.P., Upadhyay, N.R. (2023). Long Short-Term Memory-Driven Recurrent Neural Network for Real-Time Stock Monitoring and Prediction. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_66
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DOI: https://doi.org/10.1007/978-981-99-0085-5_66
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