Research ArticleOpen accessIn the abstractA Generalized Discrete-Time Long Memory Volatility Model for Financial Stock ExchangeChin Wen CheongWe proposed a simple way to combine a few long memory models in financial market volatility modeling using daily, range and high frequency data. This model was able to fit the return, range of daily return or realized volatility under a parametric heavy-tailed distribution. Model was flexible to include additional volatility information as the contemporaneous variables. Empirical results found that … …American Journal of Applied Sciences, Vol. 4, Issue 12 · 31 Dec 2007 · 6,869 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordPredicting CO2 Emissions Using Multivariate Regression: Assessing the Impact of Pandemics and Industrial RevolutionsYoussef Mekki, Chouaib Moujahdi, Noureddine Assad, Aziz DahbiThis paper explores the prediction of CO2 emissions usingMultivariate Regression models, incorporating the influence of significanthistorical events such as pandemics and industrial revolutions. Whileexisting research primarily focuses on carbon emissions alone in forecastingmodels, this study emphasizes the importance of incorporating multiplefactors for a comprehensive understanding of CO2 dynamics. Beyondemissions, factors such as socioeconomic indicators, industrial activities,environmental policies, and historical events … …Journal of Computer Science, Vol. 21, Issue 7 · 23 Jul 2025 · 3,426 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordForecasting 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 PDFCite
Research ArticleOpen accessAll words, across the recordForecasting the Diffuse Solar Radiation by using a New Modeling over Saudi ArabiaSamy A. KhalilWithin the arranging and execution assessment of solar energy projects, particularly within the planning and estimation of solar photovoltaic sources as elective energy sources within the future, exact evaluation of Diffuse Solar Radiation (DSR) is one of the essential and basic issues. The target of this article is to use the new model to predict DSR in four different locations … …American Journal of Applied Sciences, Vol. 18, Issue 1 · 5 May 2021 · 7,352 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordAn Empirical Study of Robust Modified Recursive Fits of Moving Average ModelsMohamed Ali Ismail, Hend A. Auda, Joe W. McKean, Mahmoud Mohamed SadekThe time-series Moving Average (MA) model is a nonlinear model; see, for example. For traditional Least Squares (LS) fits, there are several algorithms to use for computing its fit. Since the model is nonlinear, Fuller discusses a Newton-type step algorithm. proposed a recursive algorithm based on a sequence of three linear LS regressions. In this study, we robustify Koreisha and … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 28 Jul 2022 · 5,859 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordForecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA ModelQasem Abu Al-Haija, Qian Mao, Kamal Al NasrIn this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results … …Journal of Computer Science, Vol. 15, Issue 4 · 19 Apr 2019 · 8,599 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordArtificial Neural Network Based Model for Forecasting Sugar Cane ProductionO. O. Obe, D. K. ShangodoyinProblem statement: The global need for alternative energy source has necessitated the exploration of vast organic agricultural products with a view of processing them for the production of ethanol in commercial quantity. To ascertain a sustainable production of ethanol from processed sugar cane, a predictive model based on non-linearity nature of its production is imperative. This is due to unavailability … …Journal of Computer Science, Vol. 6, Issue 4 · 30 Apr 2010 · 7,268 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordNeural Networks Based Nonlinear Time Series Regression for Water Level Forecasting of Dungun RiverSiti Hajar Arbain, Antoni WibowoThe Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the … …Journal of Computer Science, Vol. 8, Issue 9 · 13 Aug 2012 · 7,792 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordNovel Method of the Combination of Forecasts Based on Rough SetsEslam Faik Ahmed, Wang Jia Yang, Maan Younis AbdullahProblem statement: A study in Analytic Hierarchy Process (AHP) had shown the problem of quantify the qualitative and the side Combined. Approach: So that problems were better resolved. The rough sets theory and AHP was introduced in the study, furthermore, these were united to create a completely new method of combination forecasts. Results: The results of numerical examples were shown … …Journal of Computer Science, Vol. 5, Issue 6 · 30 Jun 2009 · 7,005 accessesView PDFCite