9 results for “deep price”

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Research ArticleOpen accessEvery word in the titleDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsImplementing a Hybrid VMD–BiTCN–BiGRU Architecture With Intelligent Optimization for Agricultural Commodity Price Prognostication in Gujarat MarketsJignesh Hirapara, Ratnesh Kumar Namdeo, Ankit Kumar Dubey et al.In the agricultural sector, the prices of pulses require adequate forecasting to plan farming activities, store and market, and guide policy-making. However, the pulse price series is highly volatile, seasonal, nonstationary, and nonlinear, and thus conventional linear time series models are not necessarily very effective. In the suggested framework, the raw price data series is decomposed using Variational Mode Decomposition … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Aug 2026 · 1,021 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Dynamic Lifelong Learning Network for Real-Time Stock Prediction With Reinforcement TuningTalent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Otlhapile DinakanyaneCompanies and investors require accurate market forecasting to make more informed decisions. Traditional methods for predicting stock market performance have become less useful considering the dynamic and volatile nature that characterizes stock markets today. In this paper, we propose an innovative Dynamic Lifelong Learning Network for stock market prediction. The model incorporates a hybrid convolutional long short-term memory with an … …Journal of Computer Science, Vol. 22, Issue 2 · 20 Feb 2026 · 1,822 accessesView PDF Cite

Research ArticleOpen accessIn the abstractComparative Study of Garuda Indonesia Stock Price Prediction Using SVM, LSTM and Multiple Linear RegressionMuhammad Naufal Luthfi, Evaristus Didik MadyatmadjaStock shares are one of the investment products or tools that have been used by many people. Shares have interesting options for saving or investment and are able to provide attractive returns based on corporate or company growth. Many factors can affect the share prices, whether internal or external companies. This research was conducted on Garuda Indonesia's stock price with … …Journal of Computer Science, Vol. 21, Issue 4 · 25 Mar 2025 · 5,032 accessesView PDF Cite

Research ArticleOpen accessIn the abstractStock Price Classification Based on Hybrid Feature Selection MethodSrivinay, Manujakshi B. C., Mohan Govindsa Kabadi et al.In recent years, investors and traders have used Technical Indicators (TIs) to forecast the stock market. An accurate classification model is required in the stock market to gain more profit. Selecting relevant TIs for the stock market remains a hot research topic. The proposed work aims to identify important technical indicators. Therefore, the proposed work considers a hybrid feature selection … …Journal of Computer Science, Vol. 19, Issue 2 · 10 Feb 2023 · 6,101 accessesView PDF Cite

Research ArticleOpen accessIn the abstractIraqi Stock Market Prediction using Proposed Model of Convolution Neural NetworkSama Hayder Abdulhussein, Nashaat Jasim Al-Anber, Hayfaa Abdulzahra AteeThe stock market, sometimes known as the share market, is a marketplace for stock sellers and buyers. A stock exchange is defined as a mechanism through which stockbrokers can purchase and sell shares, bonds, and other assets. Many businesses, regardless of their industries or domains, make their shares or stocks available on the stock exchange. For value investors, predicting the … …Journal of Computer Science, Vol. 18, Issue 5 · 23 May 2022 · 6,530 accessesView PDF Cite

Research ArticleOpen accessIn the abstractForecasting of Multistep Multivariate Financial Data Through GSO Algorithm Infused Vanilla LSTM ModelNikhitha Pai, Ilango Velchamy, Nithya B RameshPredicting time series in the financial domain requires using models that can do sequence processing tasks. One such model is the LSTM model which belongs to the family of deep learning networks. The performance measure of LSTM in a regression task is to bring down the error in the predicted values. In this study, a novel fused LSTM model integrating … …Journal of Computer Science, Vol. 19, Issue 7 · 10 Jul 2023 · 6,074 accessesView PDF Cite

Research ArticleOpen accessIn the abstractStock price prediction using Generative Adversarial NetworksHungChun Lin, Chen Chen, GaoFeng Huang, Amir JafariDeep learning is an exciting topic. It has been utilized in many areas owing to its strong potential. For example, it has been widely used in the financial area which is vital to the society, such as high-frequency trading, portfolio optimization, fraud detection and risk management. Stock market prediction is one of the most popular and valuable areas in finance. … …Journal of Computer Science, Vol. 17, Issue 3 · 2 Apr 2021 · 17,700 accessesView PDF Cite

Review ArticleOpen accessIn the abstractSome Aspects of Modern DronesRelly Victoria Virgil PetrescuToday drones have become so important that everyday life can no longer be conceived without them. The paper wishes to present some essential aspects regarding modern drones, their capabilities and their uses. Drones and minnies drones have penetrated deep into technology, industry, agriculture, the military and police area, security services, security and control, aerial surveillance, signaling, aerial filming, cartography, danger … …Journal of Aircraft and Spacecraft Technology, Vol. 5, Issue 1 · 16 Jun 2021 · 8,444 accessesView PDF Cite