Using support vector machine with a hybrid feature selection method to the stock trend prediction

Lee, Ming-Chi

doi:10.1016/j.eswa.2009.02.038

Cited by 256 publications

(115 citation statements)

References 26 publications

(33 reference statements)

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“…Forecasting methodologies include correlation analysis [60], moving averaging models [61,62,63], logistic regression [64], artificial neural networks [65,66,67], support vector machines [68], and decision tree analysis [69,70]. This section presents simulation results from applying the generalized golden ratio to one commonly used moving average model -the moving average crossover.…”

Section: Simulationmentioning

confidence: 99%

The Extended Golden Section and Time Series Analysis

Agaian¹,

Gill²

2017

FSP

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The Golden ratio has played an important role in musical composition, architecture, visual art, science, and increasingly in signal processing [1,2,3]. Underlying many of these applications are several extensions of the golden proportions including the Golden p-Section by Stakhov, the generalized Golden section by Bradley, and others [4,5]. In this paper we review and introduce generalizations of the Golden ratio. We show that there exists a fundamental connection between the limit of two consecutive terms of recursive sequences, the generalized (p, q)-Golden ratio and the Golden ratio generated by the characteristic equation. We apply these generalizations to forecasting financial time series to illustrate one of their applications in signal processing.

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Section: Simulationmentioning

confidence: 99%

The Extended Golden Section and Time Series Analysis

Agaian¹,

Gill²

2017

FSP

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“…The subset of selected features is expected to be a family of attributes in which each element has the largest significance degree in S. However, a reduct can not always be obtained by this method in decision systems. Table 1, where 18 } is the universe of discourse, This example indicates that the evaluation function based on dependency is not an effective measure for assessing significance of attributes. In the following an approach to feature selection is proposed by introducing an efficient evaluation function.…”

Section: Definition 4 (Evaluation Function Based On Dependency) Letmentioning

confidence: 99%

Feature Selection in Decision Systems Based on Conditional Knowledge Granularity

Deng¹,

Yang²,

Hu³

2011

IJCIS

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Feature selection is an important technique for dimension reduction in machine learning and pattern recognition communities. Feature evaluation functions play essential roles in constructing feature selection algorithms. This paper introduces a new notion of knowledge granularity, called conditional knowledge granularity, reflecting relationship between conditional attributes and decision attribute. An evaluation function to measure significance of conditional attributes is proposed and equivalent characterization of attribute reduction is established based on the conditional knowledge granularity. An optimal algorithm for feature selection is developed on the basis of the proposed evaluation function. Furthermore, a novel approach to performing feature selection in an inconsistent decision system is put forward through establishing a rough communication between the inconsistent decision system and a consistent decision system. Simulated experiments verifies feasibility and efficiency of the proposed technique.

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“…Lee (Lee 2009) developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method to predict the trend of stock markets. The proposed hybrid feature selection method, named F-score and Supported Sequential Forward Search (F_SSFS), combines the advantages of filter methods and wrapper methods to select the optimal feature subset from original feature set.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Olatunji¹,

Al-Ahmadi²,

Elshafei³

et al. 2013

IJIIS

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Abstract:In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.

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Using support vector machine with a hybrid feature selection method to the stock trend prediction

Cited by 256 publications

References 26 publications

The Extended Golden Section and Time Series Analysis

The Extended Golden Section and Time Series Analysis

Feature Selection in Decision Systems Based on Conditional Knowledge Granularity

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

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