Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting

Huang, Shian-Chang; Wu, Tung-Kuang

doi:10.1016/j.eswa.2007.09.027

Cited by 65 publications

(22 citation statements)

References 22 publications

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“…They concluded that the predictive performance of the H2CBR system is promising and also the most preferred hybrid CBR for short-term bank failure prediction of Chinese listed companies is based on the ranking-order preference function. Other examples of this type of comparisons are done by [248][249][250][251][252][253][254][255][256][257][258][259][260][261][262][263][264][265][266]. Table 18 presents the brief results of these comparisons.…”

Section: Financial Prediction and Planningmentioning

confidence: 98%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Financial Prediction and Planningmentioning

confidence: 98%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

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“…For C and k, it is not known beforehand which values of C and k are the best for one problem. Currently, some kinds of parameter search approach are employed such as cross-validation via parallel grid-search, genetic algorithm, heuristics search, and inference of model parameters within the Bayesian evidence framework (Gestel et al, 2001;Huang & Wu, 2008;Hsu, Chang, & Lin, 2004;Min et al, 2006), and for the median-sized problems, cross-validation might be the most reliable way for model parameter selection. Thus, this study prefers a grid-search on (C, k) using fivefold cross-validation.…”

Section: Parameter Optimizationmentioning

confidence: 99%

Integration of similarity measurement and dynamic SVM for electrically evoked potentials prediction in visual prostheses research

Peng

et al. 2011

Expert Systems with Applications

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“…Beside that, some other wrapper approaches have been proposed. The method in Huang and Wu (2008) utilizes genetic algorithm to prune irrelevant and noisy features for forecasting. Neural network based feature selection methods were present in Crone and Kourentzes (2010), Wong and Versace (2012).…”

Section: Brief Review Of Feature Selection and Causal Discovery Methomentioning

confidence: 99%

Using causal discovery for feature selection in multivariate numerical time series

Sun

Liu

et al. 2014

Mach Learn

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Time series data contains temporal ordering, which makes its feature selection different from the normal feature selection. Feature selection in multivariate time series has two tasks: identifying the relevant features and finding their effective window sizes of lagged values. The methods extended from normal feature selection methods do not solve this twodimensional feature selection problem since they do not take lagged observations of features into consideration. In this paper, we present a method using the Granger causality discovery to identify causal features with effective sliding window sizes in multivariate numerical time series. The proposed method considers the influence of lagged observations of features on the target time series. We compare our proposed feature selection method with several normal feature selection methods on multivariate time series data using three well-known modeling methods. Our method outperforms other methods for predicting future values of target time series. In a real world case study on water quality monitoring data, we show that the features selected by our method contain four out of five features used by domain experts, Editors:

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Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting

Cited by 65 publications

References 22 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Integration of similarity measurement and dynamic SVM for electrically evoked potentials prediction in visual prostheses research

Using causal discovery for feature selection in multivariate numerical time series

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