Penalized ensemble feature selection methods for hidden associations in time series environments case study: equities companies in Saudi Stock Exchange Market

Survey of feature selection and extraction techniques for stock market prediction

“…Subsequently, we excluded 83 articles that did not mention the feature selection methods applied. Therefore, we obtained 32 relevant papers (27 in journals (Alsubaie et al 2019;Aloraini 2015;Li et al 2022;Kumar et al 2016Nabi et al 2019;Yuan et al 2020;Shen and Shafiq 2020;Haq et al 2021;Sun et al 2019;Hao 2017, 2020;Gunduz et al 2017;Siddique and Panda 2019;Singh and Khushi 2021;Ampomah et al 2020Ampomah et al , 2021Qolipour et al 2021;Das et al 2019;Tang et al 2018;Chong et al 2017;Bhanja and Das 2022;Xie and Yu 2021;Dami and Esterabi 2021;Gunduz 2021;Barak et al 2017;Farahani and Hajiagha 2021), and 5 in conference proceedings (Botunac et al 2020;Cai et al 2012;Labiad et al 2016;Rana et al 2019;Iacomin 2015). Figure 2 illustrates the article selection method.…”

“…In some studies, other embedded methods, such as SVM and LR models, have been applied as feature selection techniques to identify proper feature subsets as inputs to deep generative models (Haq et al 2021). Another study (Aloraini 2015) used the lasso estimation for feature selection and regularization processes to select the best subset of predictors for each bank in the Saudi stock market. In Cai et al (2012), a restricted Boltzmann machine (RBM) was applied as a feature extractor.…”

“…The correlation coefficient, such as the Pearson correlation coefficient (PCC) and Spearman rank correlation, is the simplest way to calculate the relevance score be- tween a feature and a target variable ( f, t ). Aloraini ( 2015 ) applied the Pearson and Spearman correlations as part of the ensemble feature selection process to rank 11 features, which are the daily open prices of 11 stocks. They combined univariate methods with other feature selection methods to identify hidden relationships be- tween predictors.…”

Data Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review

“…The works [12], [13] express the contribution in monitoring and control sector. [14], [15] depicts the research application on management and strategic planning domain. The authors of [16], [17] works state the use of application in personal assistance field.…”

“…The tabular representation of the contemporary models reviewed in this section follows (see Table 1). [10], [11] Field-of-medicine; [12], [13] Monitoring-and-control sector; [14], [15] Management-and-strategic-planning domain; [16], [17] Personal-assistance-field; [18], [19] Ubiquitous-environmental-information-domain. [2], [7], [22] Surveys [27] Network-intrusion-detection field Unsupervised [23] Univariate Outlier-detection [24], [25], [26] Multivariate By example models, can't react to the changes occurred in the correlated components considered [27], [28] Multivariate solo monitoring of components using ensemble scalar CDT, every single component of the data stream is inspected to detect concept drift in a multivariate data, can't react to the changes occurred in the correlated components considered [29], [30] Multivariate nonparametric density models [31], [32], [26] Multivariate 'Pure' detectors, outperform with a low volume of data Supervised [33] Machine learning Uses commonly selected sequences as Hidden Markov Models, limits to define simple patterns.…”