Innovations in Applied Engineering and Technology · 2023 · 59 citations · 12 references
ComputingData ScienceFinanceForecastingMachine LearningPredictive AnalyticsDeep LearningFinancial ForecastingLoss FunctionNeural NetworksTime Series ForecastingTime Series PredictionAdam OptimizationDeep Learning-based ForecastingFinancial Time Series ForecastingHistorical Price Data AnalysisLong Short-term Memory NetworksNeural ForecastingNeural Time Series ForecastingRecurrent Neural Network Forecasting
This research explores the application of Long Short-Term Memory (LSTM) networks for stock market analysis and prediction, focusing on four major technology stocks: Apple Inc. (AAPL), Google LLC (GOOG), Microsoft Corporation (MSFT), and Amazon.com Inc. (AMZN). Historical stock price data from Yahoo Finance spanning from January 1, 2012, to the present is utilized. The study aims to develop and evaluate an LSTM-based prediction model for forecasting future stock prices. The LSTM model consists of two LSTM layers with 128 and 64 units, respectively, followed by two dense layers. The model is trained using the Adam optimizer and mean squared error (MSE) loss function. Evaluation of the model is done using the root mean squared error (RMSE) metric. The results demonstrate the potential of LSTM models in capturing complex patterns in stock price movements and making reasonably accurate predictions.
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Klaus Greff, Rupesh K. Srivastava, Jan Koutník et al. · IEEE Transactions on Neural Networks and Learning Systems · 2016 · 6.8K citations · Full text
Stock price prediction using LSTM, RNN and CNN-sliding window model
Sreelekshmy Selvin, R. Vinayakumar, E. A. Gopalakrishnan et al. · 2017 · 1K citations
Stock Market Prediction Using LSTM Recurrent Neural Network
Adil Moghar, Mhamed Hamiche · Procedia Computer Science · 2020 · 620 citations · Full text
Computing, Finance, Forecasting +16