Kernel Linkage Support Vector Regression For Stock Market Index Prediction And Analysis
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Abstract
The Study Proposes A Novel Method For Stock Market Index Prediction And Analysis Based On Kernel Linkage Support Vector Regression (Klsvr). The Method Pre-Processes The Data To Transform It Into Information With Which The Decision System Can Work. A Regression Model Was Built For The Nasdaq Dataset Using Support Vector Machines (Svm) On The Training Data And Testing The Model For Goodness Of Fit. The Regression Types Selected For Running The R Code Was Svm Eps-Regression, Svm Nu-Regression, Bound Constraint Svm Eps-Regression. The Training And Testing Were Done With 70-30 Combination. The Experimental Result Validates The Model Proposed Using Minimum Error Analysis Performance Measures.
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