78 results for “AI-Based Forecasting”

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EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Computable General Equilibrium Model of Southern Region in Taiwan: The Impact of the Tainan Science-Based Industrial ParkChun-Chu Liu, Chia-Yon ChenThe primary modeling tools used to analyze regional economic issues include econometric forecasting models, fixed price Input-Output (I-O) multi-sector models, Social Accounting Matrix (SAM) and Computable General Equilibrium (CGE) models. CGE models combine the advantages of econometric, I-O models and SAM strengthening the theoretical basis of the modeling effort and thus enabling more precise policy analysis. The current CGE literature … …American Journal of Applied Sciences, Vol. 1, Issue 3 · 30 Sep 2004 · 7,210 accessesView PDF Cite

Research ArticleOpen accessIn the abstractElementary Structure of Matter can be Studied with New Quantum ComputersNicolae Petrescu, Florian Ion Tiberiu PetrescuMuch has been talked about quantum computers. Today they are working on difficult simulations, so we can already congratulate the college of researchers at the Oak Ridge National Laboratory as the world's first to successfully simulate an atomic nucleus using a quantum computer. The results that were published in the physical examination letters demonstrate the huge capacity of quantum systems … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 2 · 25 Jun 2018 · 7,698 accessesView PDF Cite

Research ArticleOpen accessIn the abstractOn the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy PredictionAlireza Bahiraie, Noor Akma bt Ibrahim, A. K.M. AzharProblem statement: Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. Approach: This study … …American Journal of Applied Sciences, Vol. 6, Issue 9 · 30 Sep 2009 · 7,948 accessesView PDF Cite

Research ArticleOpen accessIn the abstractVolatility in Malaysian Stock Market: An Empirical Study Using Fractionally Integrated ApproachChin Wen CheongThis study explores the fractionally integrated (FI) time series analysis in Malaysian stock market. Four proxies of latent volatility, namely the absolute return, squared return and range-based (Parkinson and Garman and Klass) volatilities are selected for the empirical studies. In addition, the well-known FI autoregressive conditional variance (ARCH) type model is also taken into account for comparison purposes. Our empirical … …American Journal of Applied Sciences, Vol. 5, Issue 6 · 30 Jun 2008 · 6,706 accessesView PDF Cite

InvestigationOpen accessIn the abstractChanges in the Composition and Properties of Meadow Solonchaks of the Ili Alatau Foothill Plain in the Republic of Kazakhstan during a Long Postmeliorative PeriodZhainagul Yertayeva, Saginbay Kaldybaev, Aigul Kaiyrbekovna BeketovaThe optimal depth of the groundwater table and the water-salt regime is of importance in the postmeliorative period. The scope of these studies is to provide scientifically grounded assessment of previously reclaimed soils (water irrigation with a total norm of 5000 m3/ha under conditions of deep horizontal drainage, 1986-1989) in the postmeliorative period of 2015-2016 and to develop parameters for … …OnLine Journal of Biological Sciences, Vol. 17, Issue 4 · 14 Oct 2017 · 7,265 accessesView PDF Cite

Research ArticleOpen accessIn the abstractLevel of Non-Matching Primary Transplant Cases According to Repeat Transplants Cases and a New Bivariate Poisson DistributionRamalingam ShanmugamIn their practice, healthcare administrators and professionals often wonder about the non-matching level organs in transplants for the sake of future forecasting. Currently, there is no appropriate methodology to analyze the pertinent transplant data and describe the patterns. The lack of a suitable methodology in the literature originates from an incorrect impression that the primary transplant cases and the repeat … …Current Research in Medicine, Vol. 8, Issue 1 · 10 Jul 2017 · 9,151 accessesView PDF Cite

Research ArticleOpen accessIn the abstractForecast and Performance of Wind TurbinesT. Ahilan, S. Arumugham, R. Suja ManimalarProblem statement: Wind energy industry is a nonprofit organization that works hands-on with local and community based wind projects, providing technical support to create an understanding of wind energy opportunities for rural economic benefit. This study provides a detailed vision of the global wind power market and the Indian wind market in particular. It also helps in analyzing and forecasting … …American Journal of Applied Sciences, Vol. 9, Issue 2 · 2 Dec 2011 · 7,909 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordTime Series Analysis for Predicting Tea Harvest Yield: A SARIMAX-Based ApproachPallavi Nagpal, Deepika Chaudhary, Jaiteg SinghPrecise prediction of tea yield is crucial for both agricultural planning and economic forecasting. Projection of the future trends, which rely on present and historical data, is the process termed 'forecasting'. Yield prediction is fundamental for research and development. By gathering the yield data over historic times, the researchers can make their work more valuable by predicting the yield patterns. … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,891 accessesView PDF Cite

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 PDF Cite