Energy Sector Stock Price Prediction Using The CNN, GRU &amp; LSTM Hybrid Algorithm

Sulistio, Bambang; Warnars, Harco Leslie Hendric Spits; Gaol, Ford Lumban; Soewito, Benfano

doi:10.1109/iccosite57641.2023.10127847

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…LSTM and GRU models can effectively predict stock prices; the LASSO dimension reduction method performs better than PCA. In previous studies by [65] to forecast the stock price, the LSTM, bi-LSTM, GRU, and ordinary neural network In the results of studies by [66], the authors proposed using deep learning in making stock predictions. This paper compared the performance of six deep-learning algorithms to predict stock closing prices on the Indonesian Stock Exchange.…”

Section: Resultsmentioning

confidence: 99%

“…The paper proposes a trading strategy for the Moroccan market using LSTM and GRU models for short-and medium-term price prediction. Bi-LSTM and GRU models MSE 0.0018 [66] CNN-LSTM-GRU hybrid algorithm RMSE decreased by 14%, MAE reduced by 13.4%, R 2 3.9% [67] LSTM and GRU models MSE 0.57 [68] LSTM and GRU MAPE 97.37% [69] -Two-layer stacked LSTM (TLS-LSTM) -Correlation analysis between different currency pairs MSE 0.0015129 [70] Stacked-Bi-LSTM RMSE 0.025 Proposed models LSTM-GRU-LSTM-GRU stack MSE 63,44 The results of studies carried out by [68] methods use LSTM and GRU. In this paper, the authors propose eight new architectural models for stock price forecasting by identifying joint movement patterns in the stock market, which combine the LSTM and GRU models with four neural network block architectures.…”

Section: Resultsmentioning

confidence: 99%

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Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks

Sujatna,

Karno,

Hastomo

et al. 2023

Knowl. Eng. Data Sci.

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The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of LSTM and GRU. Each model performs the training process in four epochs (200, 500, 750, and 1000). The results of observations in this study show that long-term predictions (28 days ahead) using four stacks of LSTM-GRU and daily training accumulation techniques produce better accuracy than the general method (using multiple outputs). From the observations we have made for predictions for the next 28 days, the model with the LGLG stack arrangement (LSTM-GRU-LSTM-GRU) produces the best accuracy at epoch 750 with an MSE LSTM-GRU 63.43762863. This study will undoubtedly continue in order to achieve even better precision, either by utilizing a new design or by further improving the technology we are now employing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks

Sujatna,

Karno,

Hastomo

et al. 2023

Knowl. Eng. Data Sci.

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“…As a result, each has its own advantages, with GRU having a faster training speed and LSTM having a higher prediction accuracy. To combine the advantages of both, the GRU-LSTM hybrid model is constructed and its effectiveness is demonstrated [31,32].…”

Section: Pso-gru-lstm Modulementioning

confidence: 99%

A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree

et al. 2023

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Wind power is an essential component of renewable energy. It enables the conservation of conventional energy sources such as coal and oil while reducing greenhouse gas emissions. To address the stochastic and intermittent nature of ultra-short-term wind power, a combined prediction model based on variational mode decomposition (VMD) and gradient boosting regression tree (GBRT) is proposed. Firstly, VMD is utilized to decompose the original wind power signal into three meaningful components: the long-term component, the short-term component, and the randomness component. Secondly, based on the characteristics of these three components, a support vector machine (SVM) is selected to predict the long-term and short-term components, while gated recurrent unit-long short-term memory (GRU-LSTM) is employed to predict the randomness component. Particle swarm optimization (PSO) is utilized to optimize the structural parameters of the SVM and GRU-LSTM combination for enhanced prediction accuracy. Additionally, a GBRT model is employed to predict the residuals. Finally, the rolling predicted values of the three components and residuals are aggregated. A deep learning framework using TensorFlow 2.0 has been built on the Python platform, and a dataset measured from a wind farm has been utilized for learning and prediction. The comparative analysis reveals that the proposed model exhibits superior short-term wind power prediction performance, with a mean squared error, mean absolute error, and coefficient of determination of 0.0244, 0.1185, and 0.9821, respectively.

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