Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934409
Zineb Belhallaj, M. Elomari, S. Melliani, L. S. Chadli
In this paper, our purpose is to solve the intuitionistic fuzzy convolution Volterra partial integro-differential equation using the intuitionistic fuzzy Laplace transform (FLTM) method under strongly Hukuhara differentiability, the intuitionistic fuzzy convolution operator is proposed and the associated theorem is given which is helpful for solving IFPVIDEs. Finally, the effectiveness and applicability of the presented method is studied with the help of a numerical example.
{"title":"On intuitionistic fuzzy laplace transforms for solving intuitionistic fuzzy partial Volterra integro-differential equations","authors":"Zineb Belhallaj, M. Elomari, S. Melliani, L. S. Chadli","doi":"10.1109/ICOA55659.2022.9934409","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934409","url":null,"abstract":"In this paper, our purpose is to solve the intuitionistic fuzzy convolution Volterra partial integro-differential equation using the intuitionistic fuzzy Laplace transform (FLTM) method under strongly Hukuhara differentiability, the intuitionistic fuzzy convolution operator is proposed and the associated theorem is given which is helpful for solving IFPVIDEs. Finally, the effectiveness and applicability of the presented method is studied with the help of a numerical example.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129162247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934719
A. Oukaira, Djallel Eddine Touati, Ahmad Hassan, Mohamed Ali, Y. Savaria, A. Lakhssassi
In this paper, we propose a thermal profile based on the finite element method (FEM). The proposed model is used to predict the temperature profile of the Xilinx™ SPARTAN-3E Field-Programmable Gate Array (FPGA) board during one day. In addition, thermal measurements based on infrared thermography are performed to validate our thermal profiles. These predicted profiles are compared to the temperature maps obtained with a thermal camera over 24 hours. A good agreement, with a maximum error of 1.8 °C, between the predicted and measured temperatures is obtained, which helps a lot in the proper functioning and the thermal management of the system-on-chips (SoC).
{"title":"FEM-based Thermal Profile Prediction for Thermal Management of System-on-Chips","authors":"A. Oukaira, Djallel Eddine Touati, Ahmad Hassan, Mohamed Ali, Y. Savaria, A. Lakhssassi","doi":"10.1109/ICOA55659.2022.9934719","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934719","url":null,"abstract":"In this paper, we propose a thermal profile based on the finite element method (FEM). The proposed model is used to predict the temperature profile of the Xilinx™ SPARTAN-3E Field-Programmable Gate Array (FPGA) board during one day. In addition, thermal measurements based on infrared thermography are performed to validate our thermal profiles. These predicted profiles are compared to the temperature maps obtained with a thermal camera over 24 hours. A good agreement, with a maximum error of 1.8 °C, between the predicted and measured temperatures is obtained, which helps a lot in the proper functioning and the thermal management of the system-on-chips (SoC).","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130644655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934507
Khalil Ibrahim Hamzaoui, M. Gabli, L. Peyrodie
The Human posture is defined by the ability to maintain a stable vertical position of balance while keeping the feet fixed relative to the ground. An accurate understanding of the equilibrium conditions is an essential point to dimension and model the forces in the exoskeleton structures. Several experiments were performed in this sense. However, the resulting data are quite vague and uncertain, which could contribute to the error in the equilibrium description. In this paper, we focus on the folding. Our objective is to find the factors and mechanisms that influence this fallback in order to improve its anticipation. We have considered an approach based on fuzzy logic to better explain the uncertain and ambiguous aspect of the data, and on data mining algorithms to find the factors that influenced the fall. The results of our methodology showed promising associations to anticipate this type of fall.
{"title":"An intelligent decision support system to anticipate the fall of a structure for paraplegic patients","authors":"Khalil Ibrahim Hamzaoui, M. Gabli, L. Peyrodie","doi":"10.1109/ICOA55659.2022.9934507","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934507","url":null,"abstract":"The Human posture is defined by the ability to maintain a stable vertical position of balance while keeping the feet fixed relative to the ground. An accurate understanding of the equilibrium conditions is an essential point to dimension and model the forces in the exoskeleton structures. Several experiments were performed in this sense. However, the resulting data are quite vague and uncertain, which could contribute to the error in the equilibrium description. In this paper, we focus on the folding. Our objective is to find the factors and mechanisms that influence this fallback in order to improve its anticipation. We have considered an approach based on fuzzy logic to better explain the uncertain and ambiguous aspect of the data, and on data mining algorithms to find the factors that influenced the fall. The results of our methodology showed promising associations to anticipate this type of fall.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121364606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information and communication technologies (ICT) allow the creation of smart cities to provide better quality services to citizens by exchanging information with the general public. In Morocco, the waste management is the primary challenge for the competent authority to reduce the amount of solid waste generated and satisfy the environmental regulations. The waste collection and treatment plan is the first pillar to optimize in order to better manage the quantities of waste produced by different industrial activities. Smart technologies were identified as alternative solution having the required qualifications for the creation the smart cities. They haves great potential to increase the efficiency and quality of waste collection. High costs and low efficiency are the two main challenges of smart garbage collection. An inconsequent management leads to resources waste at all levels. For example, the city resources are misused and a colossal amount of gasoline is wasted every day. This problem can be solved by managing and protecting all storage spaces using machine learning technics. A key goal of machine learning is the development of algorithms to make future predictions. Machine Learning Based Automatic Waste Recycling Framework has been proposed to classify and separate materials in a mixed recycling application to improve the separation of complex waste. The main purpose of the present paper is to assess machine learning algorithms used in recycling systems. As result, Machine Learning (ML) and Internet of Things (IoT) were proposed for smart waste management to surround the waste collection issue in the smart city. Powered devices can be installed in waste containers, including recycling bins, and provide real-time data on waste-generation.
{"title":"Waste solid management using Machine learning approch","authors":"Gouskir Lahcen, Edahbi Mohamed, Gouskir Mohammed, Hachimi Hanaa, Abouhilal Abdelmoula","doi":"10.1109/ICOA55659.2022.9934356","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934356","url":null,"abstract":"Information and communication technologies (ICT) allow the creation of smart cities to provide better quality services to citizens by exchanging information with the general public. In Morocco, the waste management is the primary challenge for the competent authority to reduce the amount of solid waste generated and satisfy the environmental regulations. The waste collection and treatment plan is the first pillar to optimize in order to better manage the quantities of waste produced by different industrial activities. Smart technologies were identified as alternative solution having the required qualifications for the creation the smart cities. They haves great potential to increase the efficiency and quality of waste collection. High costs and low efficiency are the two main challenges of smart garbage collection. An inconsequent management leads to resources waste at all levels. For example, the city resources are misused and a colossal amount of gasoline is wasted every day. This problem can be solved by managing and protecting all storage spaces using machine learning technics. A key goal of machine learning is the development of algorithms to make future predictions. Machine Learning Based Automatic Waste Recycling Framework has been proposed to classify and separate materials in a mixed recycling application to improve the separation of complex waste. The main purpose of the present paper is to assess machine learning algorithms used in recycling systems. As result, Machine Learning (ML) and Internet of Things (IoT) were proposed for smart waste management to surround the waste collection issue in the smart city. Powered devices can be installed in waste containers, including recycling bins, and provide real-time data on waste-generation.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934114
Khalid El Moutaouakil, B. Jabir, N. Falih
Agriculture 4.0 is a technological revolution in the agricultural field which consists of digitizing agricultural processes and taking advantage of advanced digital technologies in order to boost productivity, optimize resources, adapt to climate change and avoid food waste. Advanced technologies related to artificial intelligence, Big data Analytics, Cloud Computing and the Internet of Things constitute the lever of the agriculture transformation that allows a predictive and a strategic analysis of the massive data collected for smart and optimal management of agricultural plots. In this context, we share results from a detailed study on the latest advanced digital technologies used in the different agricultural sectors, the agricultural potential of the Beni Mellal-Khenifra region, the limits and the challenges facing the application of these technologies in the different agricultural sectors. This work is also intended to be a roadmap for researchers wishing to understand more about this new mode of agriculture.
{"title":"Agriculture 4.0: Literature Review and Application Challenges in the “Beni Mellal-Khenifra” region","authors":"Khalid El Moutaouakil, B. Jabir, N. Falih","doi":"10.1109/ICOA55659.2022.9934114","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934114","url":null,"abstract":"Agriculture 4.0 is a technological revolution in the agricultural field which consists of digitizing agricultural processes and taking advantage of advanced digital technologies in order to boost productivity, optimize resources, adapt to climate change and avoid food waste. Advanced technologies related to artificial intelligence, Big data Analytics, Cloud Computing and the Internet of Things constitute the lever of the agriculture transformation that allows a predictive and a strategic analysis of the massive data collected for smart and optimal management of agricultural plots. In this context, we share results from a detailed study on the latest advanced digital technologies used in the different agricultural sectors, the agricultural potential of the Beni Mellal-Khenifra region, the limits and the challenges facing the application of these technologies in the different agricultural sectors. This work is also intended to be a roadmap for researchers wishing to understand more about this new mode of agriculture.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121910454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934593
E. Tarasova, N. Grigoreva
The paper proposes for consideration an online scheduling model for single processor with a deadlines and minimization of the total delay. A new LJSF algorithm has been proposed that takes into account the size of the jobs entering the process and is adapted to cases of large jobs. In comparison with existing algorithms, LJSF improved the results on average by 3% - 20% in more than 40% of examples for different testing groups, while in other cases the values of the objective functions were close with a deviation of no more than 2%.
{"title":"Accounting for large jobs for a single-processor online model","authors":"E. Tarasova, N. Grigoreva","doi":"10.1109/ICOA55659.2022.9934593","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934593","url":null,"abstract":"The paper proposes for consideration an online scheduling model for single processor with a deadlines and minimization of the total delay. A new LJSF algorithm has been proposed that takes into account the size of the jobs entering the process and is adapted to cases of large jobs. In comparison with existing algorithms, LJSF improved the results on average by 3% - 20% in more than 40% of examples for different testing groups, while in other cases the values of the objective functions were close with a deviation of no more than 2%.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934280
El Miloud Smaili, Salma Azzouzi, My El Hassan Charaf
The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, especially with the restrictions imposed by the advent of the COVID-19 pandemic. However, even though the number of registrants to MOOCs is quite considerable, only 10% of the learners complete the MOOC and obtain a certification. This phenomenon leads us to dig deeper to wonder about the means to avoid the high dropout rate of learners in such platforms. For this purpose, we suggest in this paper two complementary systems: a preventive system coupled with a proactive system to personalize the learners' pathways according to their specific needs and prior knowledge. The optimization of the pathways will be handled using a metaheuristic optimization algorithm called: Cuckoo Search Algorithm.
{"title":"An Optimized Adaptive Learning Approach Based on Cuckoo Search Algorithm","authors":"El Miloud Smaili, Salma Azzouzi, My El Hassan Charaf","doi":"10.1109/ICOA55659.2022.9934280","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934280","url":null,"abstract":"The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, especially with the restrictions imposed by the advent of the COVID-19 pandemic. However, even though the number of registrants to MOOCs is quite considerable, only 10% of the learners complete the MOOC and obtain a certification. This phenomenon leads us to dig deeper to wonder about the means to avoid the high dropout rate of learners in such platforms. For this purpose, we suggest in this paper two complementary systems: a preventive system coupled with a proactive system to personalize the learners' pathways according to their specific needs and prior knowledge. The optimization of the pathways will be handled using a metaheuristic optimization algorithm called: Cuckoo Search Algorithm.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114641545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934558
Assia Najm, A. Zakrani, A. Marzak
There is evidence that precise prediction of the software development effort plays a crucial role in properly monitoring and managing software projects. Researchers have suggested many software effort estimation techniques. Nonetheless, none of these methods performed well in all circumstances. Ensemble models have been recently proposed in the literature to overcome the significant drawbacks of single machine learning approaches. In this study, we proposed a novel model, the ensemble of optimal additive cluster-based fuzzy regression trees for software development effort prediction. We performed an empirical evaluation using four datasets and the 30% holdout cross-validation technique. We compared the performance of our proposed ensemble model to the c-fuzzy regression tree, the bagged c-fuzzy regression tree model, the ensemble of optimal trees, random forest, and regression trees. Our suggested model outperforms all the compared models in Pred (25%), MMRE, and MdMRE in all employed datasets.
{"title":"Optimal Additive C-Fuzzy Regression Trees for Software Development Effort Prediction","authors":"Assia Najm, A. Zakrani, A. Marzak","doi":"10.1109/ICOA55659.2022.9934558","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934558","url":null,"abstract":"There is evidence that precise prediction of the software development effort plays a crucial role in properly monitoring and managing software projects. Researchers have suggested many software effort estimation techniques. Nonetheless, none of these methods performed well in all circumstances. Ensemble models have been recently proposed in the literature to overcome the significant drawbacks of single machine learning approaches. In this study, we proposed a novel model, the ensemble of optimal additive cluster-based fuzzy regression trees for software development effort prediction. We performed an empirical evaluation using four datasets and the 30% holdout cross-validation technique. We compared the performance of our proposed ensemble model to the c-fuzzy regression tree, the bagged c-fuzzy regression tree model, the ensemble of optimal trees, random forest, and regression trees. Our suggested model outperforms all the compared models in Pred (25%), MMRE, and MdMRE in all employed datasets.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934252
Ibtissam Medarhri, Mohamed Hosni, Najib Nouisser, F. Chakroun, Khalid Najib
Accurately predicting upcoming values of stock market index is a very difficult task due to instability of financial stock markets. In fact, an accurate prediction helps brokers to make adequate decision on buying or selling stock. Toward this aim, six Machine Learning (ML) techniques namely: Support Vector Regression (SVR), K-nearest Neighbor (Knn), Decision trees (DTs), Random Forest, Artificial Neural Networks (MLPs), Deep learning technique, were built to predict the future closing price for five companies that are part of the S&P500 index and the closing price of S&P500 index. Teen years of data and six new generated variables were used as inputs for our used models, which were assessed using two performance metrics and build using the grid search optimization technique. The results show that there is no best ML technique that may adopted to predict the trends of a given stock price. However, all the constructed techniques yield a very promising performance, and that the MLP and LSTM techniques, which belong to ANN family, may be considered as best techniques.
{"title":"Predicting Stock Market Price Movement using Machine Learning Techniques","authors":"Ibtissam Medarhri, Mohamed Hosni, Najib Nouisser, F. Chakroun, Khalid Najib","doi":"10.1109/ICOA55659.2022.9934252","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934252","url":null,"abstract":"Accurately predicting upcoming values of stock market index is a very difficult task due to instability of financial stock markets. In fact, an accurate prediction helps brokers to make adequate decision on buying or selling stock. Toward this aim, six Machine Learning (ML) techniques namely: Support Vector Regression (SVR), K-nearest Neighbor (Knn), Decision trees (DTs), Random Forest, Artificial Neural Networks (MLPs), Deep learning technique, were built to predict the future closing price for five companies that are part of the S&P500 index and the closing price of S&P500 index. Teen years of data and six new generated variables were used as inputs for our used models, which were assessed using two performance metrics and build using the grid search optimization technique. The results show that there is no best ML technique that may adopted to predict the trends of a given stock price. However, all the constructed techniques yield a very promising performance, and that the MLP and LSTM techniques, which belong to ANN family, may be considered as best techniques.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934755
A. Razouk, Rachid Ait daoud, Moulay El Mehdi Falloul
Maximum likelihood estimation (MLE) is often used in econometric and other statistical models despite its computational considerations and because of its strong theoretical appeal. The non-linear optimization discipline provides feasible alternative methods for calculating MLE's, especially when the special structure may be exploited, for example in probabilistic choice models. This paper examines the estimation of the financial time series model parameters named GARCH(p, q) using four numerical optimization methods and gives numerical comparisons of these methods. Among the issues considered in this paper are the theoretical background of MLE. Also, methods of approximating the Hessian are presented. These include (DFP and BFGS) and statistical approximations (BHHH).
{"title":"Numerical optimization methods for financial time series GARCH(p, q) model, a comparative approach","authors":"A. Razouk, Rachid Ait daoud, Moulay El Mehdi Falloul","doi":"10.1109/ICOA55659.2022.9934755","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934755","url":null,"abstract":"Maximum likelihood estimation (MLE) is often used in econometric and other statistical models despite its computational considerations and because of its strong theoretical appeal. The non-linear optimization discipline provides feasible alternative methods for calculating MLE's, especially when the special structure may be exploited, for example in probabilistic choice models. This paper examines the estimation of the financial time series model parameters named GARCH(p, q) using four numerical optimization methods and gives numerical comparisons of these methods. Among the issues considered in this paper are the theoretical background of MLE. Also, methods of approximating the Hessian are presented. These include (DFP and BFGS) and statistical approximations (BHHH).","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116131758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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