首页|Study Findings on Support Vector Machines Reported by Researchers at Carleton Un iversity (Novel comparative methodology of hybrid support vector machine with me ta-heuristic algorithms to develop an integrated candlestick technical analysis model)
Study Findings on Support Vector Machines Reported by Researchers at Carleton Un iversity (Novel comparative methodology of hybrid support vector machine with me ta-heuristic algorithms to develop an integrated candlestick technical analysis model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on . According t o news reporting originating from Ottawa, Canada, by NewsRx correspondents, rese arch stated, “Purpose - The proposed model has been aimed to predict stock marke t signals by designing an accurate model. In this sense, the stock market is ana lysed by the technical analysis of Japanese Candlestick, which is combined by th e following meta heuristic algorithms: support vector machine (SVM), meta-heuris tic algorithms, particle swarm optimization (PSO), imperialist competition algor ithm (ICA) and genetic algorithm (GA).” The news editors obtained a quote from the research from Carleton University: “D esign/ methodology/approach - In addition, among the developed algorithms, the mo st effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand w ith the high speed of running. In terms of the second model, SVM and ICA are exa mined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feat ure selection agent. Findings - Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA mo dels have correctly predicted more sell signals, and the SCM-PSO model has corre ctly predicted more buy signals. However, SVM-ICA has shown better performance t han other models considering executing the implemented models. Research limitati ons/implications - In this study, the authors to analyze the data the long lengt h of time between the years 2013-2021, makes the input data analysis challenging .”
Carleton UniversityOttawaCanadaNor th and Central AmericaAlgorithmsEmerging TechnologiesFinance and Investmen tInvestment and FinanceMachine LearningSupport Vector MachinesVector Mac hines
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