The strategy of investment in the stock market using modified support vector regression model

Huang, Chih-Hua; Yang, Fubin; Lee, Chien-Pang

doi:10.24200/sci.2017.4440

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…In Equations ( 4) and ( 5), C determines the tradeoff between the flatness of function f (x) and the value up to which deviations more significant than ε are tolerated; ξ i and ξ * i are positive slack variables; and |ξ| ε is the ε-insensitive loss function [14,26]. The detailed procedure for the SVR model can be found in the literature [25,36,40]. The SVR model has three main kernel functions: the Gaussian radial basis function (RBF), the polynomial function, and the sigmoid function.…”

Section: Support Vector Regressionmentioning

confidence: 99%

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

et al. 2022

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Recently, annual agricultural data have been highly volatile as a result of climate change and national economic trends. Therefore, such data might not be enough to develop good agricultural policies for stabilizing agricultural output. A good agricultural output prediction model to assist agricultural policymaking has thus become essential. However, the highly volatile data would affect the prediction model’s performance. For this reason, this study proposes a marriage in honey bees optimization/support vector regression (MBO/SVR) model to minimize the effects of highly volatile data (outliers) and enhance prediction accuracy. We verified the performance of the MBO/SVR model by using the annual total agricultural output collected from the official Agricultural Statistics Yearbook of the Council of Agriculture, Taiwan. Taiwan’s annual total agricultural output integrates agricultural, livestock and poultry, fishery, and forest products. The results indicated that the MBO/SVR model had a lower mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), and relative root mean squared error (r-RMSE) than those of the models it was compared to. Furthermore, the MBO/SVR model predicted long-term agricultural output more accurately and achieved higher directional symmetry (DS) than the other models. Accordingly, the MBO/SVR model is a robust, high-prediction-accuracy model for predicting long-term agricultural output to assist agricultural policymaking.

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Section: Support Vector Regressionmentioning

confidence: 99%

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

et al. 2022

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“…To overcome optimization problems with constraints it is called lagrange. The optimal solution can be solved by the lagrange multiplier equation which is formulated using Equation (3) [23].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Utami

Novitasari

Setiawan³

et al. 2021

SJI

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Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions.Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error.Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.

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“…Computational Intelligence methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are wildly used in this area of research considering their ability in handling hidden feature of data as well nonlinear modeling [32,33,34,35,36,37,38]. In addition, they are preferable for all temporal forecasting ranges.…”

Section: Introductionmentioning

confidence: 99%

ParDeeB: A Graph Framework for Load Forecasting Based on Parallel DeepNet Branches

2021

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Recently, energy demand forecasting has emerged as a significant area of research because of its prominent impact on greenhouse gases (GHGs) emission and global warming. The problems of load forecasting are characterized by complex and nonlinear nature and also long-term historical dependency.Up to now, several approaches from statistical to computational intelligent have been applied in this research filed. The literature agrees with the fact that deep learning approach is more capable in dealing with these characteristics among existing approaches. However, the recent state-of-the-art deep network models are not robust against different historical dependency. In this

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The strategy of investment in the stock market using modified support vector regression model

Cited by 5 publications

References 25 publications

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

ParDeeB: A Graph Framework for Load Forecasting Based on Parallel DeepNet Branches

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