Incorporating feature selection method into support vector regression for stock index forecasting

Dai, Wensheng; Shao, Yuehjen E.; Lu, Chi-Jie

doi:10.1007/s00521-012-1104-1

Cited by 28 publications

(18 citation statements)

References 40 publications

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“…Thus MARS has been widely used in various fields such as sales prediction [52], credit evaluation [53][54][55], stock price forecasting [56][57][58], software reliability analysis [59][60][61] and predicting species distribution [62,63]. However, MARS has been rarely used in port throughput forecasting in the existing literature; therefore, this paper employs MARS to determine the final input vectors for RSVR and analyze significance degrees between different factors for further port throughput generation mechanism research.…”

Section: Introductionmentioning

confidence: 99%

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Geng

Dong

et al. 2015

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Geng

Dong

et al. 2015

Neurocomputing

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“…Additionally, since the radial basis function (RBF) is the most widely used kernel function [36], this study uses it for our experimental study. The RBF can be defined as follows:

\begin{matrix} ϕ (x_{i}, x_{j}) = \exp (\frac{- {|| x_{i} - x_{j} ||}^{2}}{2 σ^{2}}), \end{matrix}

where σ denotes the width of the RBF.…”

Section: Research Methodologiesmentioning

confidence: 99%

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

Shao

2014

The Scientific World Journal

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Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.

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“…In this study, we use the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute difference (MAD) to measure the prediction capability of the models. These prediction measurements are defined as follows [26]:…”

Section: Datasets and Performance Criteriamentioning

confidence: 99%

“…The explanatory variables for the models included installed capacity, gross electricity generation, population, and the total number of customers. However, ANN has been criticized for its long training process when designing the optimal structure [25,26].In recent years, hybrid forecasting models with feature selection techniques (FSTs) have been studied for electricity sales forecasting. A hybrid model with fast ensemble empirical mode decomposition, variational mode decomposition, and ANN was investigated in forecasting electricity prices [27].…”

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confidence: 99%

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Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

Shao

Tsai

2018

Energies

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Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models.

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Incorporating feature selection method into support vector regression for stock index forecasting

Cited by 28 publications

References 40 publications

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

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