Abstract
Stock markets have been an integral part of our socioeconomic society. People invest a lot of monetary funds into them so as to earn gains. But that is not the case every time due to the ever wavering nature of the markets. To minimize the risk of loss due to drastically changing market, people have come up with many predictive models to simulate the future of stock markets. This paper presents a model that can predict the stock market. The use of stacked Long Short-Term Memory gives the model an advantage over the conventional machine learning models to provide better MSE.
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Ferni Ukrit, M., Saranya, A., Anurag, R. (2020). Stock Market Prediction Using Long Short-Term Memory. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-15-0199-9_18
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DOI: https://doi.org/10.1007/978-981-15-0199-9_18
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