Abstract
The aim this paper is to make the trader’s life easy with all kinds of exploratory data analysis and to bring the forecast with the deep learning model. We have used a different kind of analysis to come to a point on which we have built a model completely based on long short-term memory and recurrent neural network. We have also performed different types of graphs to explain the trend of the company user searches. The trend is a line graph depicting years and months based on user selection. Since forecasting has highly emerged, there has been a lot of research on this topic. The stock market is always the scientist’s favourite category to show their skills. This paper attention on the analysis and prediction of stock values that do not take care of party-political tenures, and financial tension which disturbs the stock market. This model will assist a stockholder, distinct user or the general public to make protected investments.
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Bhoite, S., Ansari, G., Patil, C.H., Thatte, S., Magar, V., Gandhi, K. (2023). Stock Market Prediction Using Recurrent Neural Network and Long Short-Term Memory. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. Lecture Notes in Networks and Systems, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-19-5331-6_65
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DOI: https://doi.org/10.1007/978-981-19-5331-6_65
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