Abstract
Forecasting the stock market is difficult because the stock price time series is so intricate. We applied long short-term memory (LSTM) algorithm, convolutional neural network (CNN) and artificial neural network (ANN) because of the following reason: Recent work provides preliminary proof that machine learning methods could locate relationships in stock price sequences that are not linear. The stock market's non-stationary and high volatility makes it difficult to foresee the trajectory time series data in the economy. We expanded the scope of our model training outside the Indian stock market. More specifically on stock prices of MAANG and Divis labs, all three models were performing really well with RMSE of LSTM being 0.42 and R2 score being 0.55, and this being the best result among all.
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Kakarla, K.P., Kiran, D.C.S., Kanchana, M. (2024). Stock Price Prediction Using LSTM, CNN and ANN. In: Zen, H., Dasari, N.M., Latha, Y.M., Rao, S.S. (eds) Soft Computing and Signal Processing. ICSCSP 2023. Lecture Notes in Networks and Systems, vol 840. Springer, Singapore. https://doi.org/10.1007/978-981-99-8451-0_12
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