A New Fuzzy Grach Model to forecast Stock Market Technical Analysis

Mustafa, Saima; Bajwa, Arfa Amjad; Iqbal, Shafqat

doi:10.31181/oresta040422196m

Cited by 16 publications

(9 citation statements)

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“…e significance of all the input variables is determined on the basis of correlation method [55,56]. All the input variables are normalized prior to applying them for better results [57]. In graphs, hours of the day are presented on x-axis, whereas load demand in megawatts (MW) is shown on y-axis.…”

Section: Resultsmentioning

confidence: 99%

Review of Short-Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods

Islam

Rasheed

Ahmed

2022

Mathematical Problems in Engineering

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Forecasting electricity load demand is critical for power system planning and energy management. In particular, accurate short-term load forecasting (STLF), which focuses on the lead time horizon of few minutes to one week ahead, can help in better load scheduling, unit commitment, and cost-effective operation of smart power grids. In the last decade, different artificial intelligence (AI)-based techniques and metaheuristic algorithms have been utilized for STLF by the researchers and scientists with varying degrees of accuracy and efficacy. Despite the benefits of implemented methods for STLF, many drawbacks and associated problems have also been observed and reported by the researchers. This paper provides a comprehensive review of hybrid deep learning models based on nature-inspired metaheuristic techniques for STLF with respect to the analysis of the results and accuracy. Moreover, it also provides the research findings and gaps that will assist the researchers to have an early awareness of all important benefits and drawbacks of these integrated STLF methods scientifically and systematically. Especially, the hybrid forecast models using artificial intelligence-based methods for smart grids are focused. Several performance indices are used to compare and report the accuracy of these techniques including mean absolute percentage error (MAPE). Multiple other parametric and exogenous variable details have also been focused to figure out the potential of the intelligent load forecasting techniques from the perspective of smart power grids.

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Section: Resultsmentioning

confidence: 99%

Review of Short-Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods

Islam

Rasheed

Ahmed

2022

Mathematical Problems in Engineering

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“…A numerical examples show the efficiency of our newly proposed method in comparison to the exact and analytical techniques that are currently used in the literature. The exact solution is matched with graphic representations of both the strong and weak solutions of the triangular linear Diophantine fuzzy system of equations.Therefore, future research will concentrate on the solution of large systems of triangle linear Diophantine fuzzy system of equations as well as a system of nonlinear equations, and their engineering and managerial application [46][47][48][49] in a triangular linear Diophantine fuzzy environment.…”

Section: Discussionmentioning

confidence: 99%

Modified Block Homotopy Perturbation Method for solving triangular linear Diophantine fuzzy system of equations

Shams

Kausar

Khan

et al. 2023

Advances in Mechanical Engineering

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Numerous real-world applications can be solved using the broadly adopted notions of intuitionistic fuzzy sets, Pythagorean fuzzy sets, and q-rung orthopair fuzzy sets. These theories, however, have their own restrictions in terms of membership and non-membership levels. Because it utilizes benchmark or control parameters relating to membership and non-membership levels, this theory is particularly valuable for modeling uncertainty in real-world problems. We propose the unique concept of linear Diophantine fuzzy set with benchmark parameters to overcome these restrictions. Different numerical, analytical, and semi-analytical techniques are used to solve linear systems of equations with several fuzzy numbers, such as intuitionistic fuzzy number, triangular fuzzy number, bipolar fuzzy number, trapezoidal fuzzy number, and hexagon fuzzy number. The purpose of this research is to solve a fuzzy linear system of equations with the most generalized fuzzy number, such as Triangular linear Diophantine fuzzy number, using an analytical technique called Homotopy Perturbation Method. The linear systems co-efficient are crisp when the right hand side vector is a triangular linear Diophantine fuzzy number. A numerical test examples demonstrates how our newly improved analytical technique surpasses other existing methods in terms of accuracy and CPU time. The triangular linear Diophantine fuzzy systems of equations’ strong and weak visual representations are explored.

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“…1, 2023: 1 -17 4 its usage is suitable for quantifying qualitative input data. (Pamučar et al, 2011(Pamučar et al, , 2012Božanić et al, 2015Božanić et al, , 2016Švadlenka et al, 2020;Stanković et al, 2020;Simić et al, 2021;Milovanović et al, 2021;Mustafa et al, 2022;Gayen et al, 2021). Fuzzy LMAW method steps for determination of weight coefficient of the criteria are shown as follows (Božanić et al, 2022).…”

Section: Lmawmentioning

confidence: 99%

Development of the MCDM fuzzy LMAW-grey MARCOS model for selection of a dump truck

Tešić¹,

Božanić²,

Puška³

et al. 2023

Rep. Mech. Eng.

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This study presents the MCDM model created for the selection of a dump truck for the needs of the army engineering units, based primarily on the truck’s construction features and purchasing and maintenance costs. In this study was used the Methodology of Additive Weights (LMAW) in Fuzzy surrounding for determination of weight coefficients of criteria, while for the selection of the optimal alternative (for a dump truck) it was used the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified by interval grey numbers. Input data for this methodology were obtained by engaging experts. Finally, the analysis was made of the sensitivity of output results of the proposed MCDM methodology to the change of weight coefficients of criteria, as well as the comparison of the obtained results with the results of other methodologies. In the conclusion, the proposed model showed stability but it was sensitive to weight coefficients change which should be taken into account by defining the same by experts.

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A New Fuzzy Grach Model to forecast Stock Market Technical Analysis

Cited by 16 publications

References 0 publications

Review of Short-Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods

Review of Short-Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods

Modified Block Homotopy Perturbation Method for solving triangular linear Diophantine fuzzy system of equations

Development of the MCDM fuzzy LMAW-grey MARCOS model for selection of a dump truck

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