2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · 2022 · 21 citations · 8 references
ComputingComputer ScienceFinanceForecastingMachine LearningPredictive AnalyticsDeep LearningNeural NetworksPattern RecognitionSupport Vector MachinesTime Series ForecastingTime Series PredictionDeep Learning For ForecastingDeep Learning For Time Series ForecastingDeep Learning-based ForecastingMachine Learning Forecasting ModelsMachine Learning-based ForecastingMachine Learning-based Price ForecastingNeural ForecastingNeural Time Series Forecasting
Stock market price prediction is one of the most challenging problems in the field of time series forecasting because of the chaotic and volatile nature of stock market data. The currently existing technology is evolving in terms of algorithmic sophistication to provide better ways to predict behavior in highly volatile and noisy environments. A new trend in the machine learning and pattern recognition fields suggests that time-series prediction should be done using a deep nonlinear topology. Many papers on stock market price prediction have analyzed various machine learning algorithms like Artificial Neural Networks, ARIMA, CNN, RNN, SVM classifier, Fuzzy classifier, Random forest, and Bayesian model. Most suggest that a variant of RNN called LSTM outperforms the other algorithms. However, there is still a lesser-explored area within LSTM variants that can further be explored. This paper compares the performance of Vanilla LSTM with the other LSTM variant called Peephole LSTM. The experiments revealed that Peephole LSTM outperforms Vanilla LSTM by a significant margin.
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Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation · 1997 · 98.1K citations
A Comparison of ARIMA and LSTM in Forecasting Time Series
Sima Siami‐Namini, Neda Tavakoli, Akbar Siami Namin · 2018 · 1.2K citations