Forecasting daily stock trend using multi-filter feature selection and deep learning

Haq, Anwar Ul; Zeb, Adnan; Lei, Zhenfeng; Zhang, Defu

doi:10.1016/j.eswa.2020.114444

Cited by 89 publications

(48 citation statements)

References 32 publications

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“…Other VIs were also important for EWT canopy estimation. This is consistent with other studies that showed that different feature selection methods increase the performance of the model and decrease the computational time [48,58,64,65]. Haq et al [64] stated that feature selection methods significantly increase model accuracy.…”

Section: Feature Selection Methodssupporting

confidence: 90%

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

et al. 2021

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The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha) were conducted in the North China Plain. The canopy reflectance was measured for all plots at 30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep Neural Network (DNN) is a special type of neural network, which has shown performance in regression analysis is compared with other machine learning (ML) models. A feature selection (FS) algorithm named the decision tree (DT) was used as the automatic relevance determination method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The results show that the DNN-MLP with R2 = 0.934, NSE = 0.933, RMSE = 0.028 g/cm2, and MAE of 0.017 g/cm2 outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to its high capacity for estimating EWT as compared to other ML methods. Our findings support the conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite the complexity of the ML models, the EWT map should help farmers by improving the real-time irrigation efficiency of wheat by quantifying field water content and addressing variability.

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Section: Feature Selection Methodssupporting

confidence: 90%

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

et al. 2021

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“…One of limitation of our research is we only four feature selection methods. For this reason, as future work, we will apply other feature selection approaches presented in [18], [19],…”

Section: F Results Comparisionmentioning

confidence: 99%

An Effective Filter Method Towards the Performance Improvement of FF-SVM Algorithm

Alshamlan

2021

IEEE Access

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Fining effective and informative biomarker genes form microarray is very challenging. In order to develop an hybrid gene selection algorithm, numerous filter feature selection algorithms have been previously reported. This research paper aims to identify the filter method that will improve the performance of our previously proposed FF-SVM algorithm to find the minimum number of accurate genes that achieves high accuracy performance. Therefore, an experiment was conducted using four different filter methods: Maximum Relevance Minimum Redundancy (mRMR), Joint Mutual Information (JMI), Fscore, and Double Input Symmetrical Relevance (DISR). This experiment was undertaken in two phases: the first phase was filter to SVM, to identify the minimum number of features (genes) which served to maximize the SVM classifier; the second phase was filter to FF-SVM, to ascertain the best suite filter method to our previously proposed FF-SVM algorithm. The result of this experiment would be the most suited filter method to the FF-SVM. In conclusion, we found that the f-score method outperformed other filter methods when combined with FF-SVM. INDEX TERMSbio-inspired, gene expression profile, gene selection firefly, hybrid-based algorithm, microarray, SVM.

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“…7. [53], is employed to choose the most relevant features. The feature ranking is estimated for all five datasets and found that the ranking is different for each stock dataset.…”

Section: Input Variables Formulasmentioning

confidence: 99%

“…Gündüz et al[52], proposed a feature selection approach using gain ratio and relief function and reported significant performance improvement in stock forecasting. Haq et al[53], proposed a multi-filter feature selection (MFFS) approach for choosing relevant technical indicators. Three different feature selection methods such as: L1 regularized logistic regression (L1-LR), SVM, and random forest (RF) are applied to rank the technical indicators and the top-ranked indicators are chosen and grouped to form the optimal feature subset.…”

mentioning

confidence: 99%

A Repairing Artificial Neural Network Model-Based Stock Price Prediction

Bai¹,

Thanabal²

2021

IJCIS

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Predicting the stock price movements based on quantitative market data modeling is an open problem ever. In stock price prediction, simultaneous achievement of higher accuracy and the fastest prediction becomes a challenging problem due to the hidden information found in raw data. Various prediction models based on machine learning algorithms have been proposed in the literature. In general, these models start with the training phase followed by the testing phase. In the training phase, the past stock market data are used to learn the patterns toward building a model that would then use to predict future stock prices. The performance of such learning algorithms heavily depends on the quality of the data as well as optimal learning parameters. Among the conventional prediction methods, the use of neural network has greatest research interest because of their advantages of self-organizing, distributed processing, and self-learning behaviors. In this work, dynamic nature of the data is mainly focused. In conventional models the retraining has to be carried out for two cases: the data used for training has higher noise and outliers or model trained without preprocessing; the learned data has to update dynamically for recent changes. In this sense, propose a self-repairing dynamic model called repairing artificial neural network (RANN) that correct such errors effectively. The repairing includes adjusting the prediction model from noise, outliers, removing a data sample, and adjusting an attribute value. Hence, the total reconstruction of the prediction model could be avoided while saving training time. The proposed model is validated with five different real-time stock market data and the results are quantified to analyze its performance.

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Forecasting daily stock trend using multi-filter feature selection and deep learning

Cited by 89 publications

References 32 publications

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

An Effective Filter Method Towards the Performance Improvement of FF-SVM Algorithm

A Repairing Artificial Neural Network Model-Based Stock Price Prediction

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