A hybrid financial trading support system using multi-category classifiers and random forest

Thakur, Manoj; Kumar, Deepak

doi:10.1016/j.asoc.2018.03.006

Cited by 52 publications

(21 citation statements)

References 32 publications

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“…The RMSE will be used to compare the proposed deep learning model with other popular learning methods, namely Support Vector Machine [18] , Decision Tree [56] , and Random Forest [57] .…”

Section: Methodsmentioning

confidence: 99%

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections

Gupta

Jain

Taneja

et al. 2021

Applied Soft Computing

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“…The RMSE will be used to compare the proposed deep learning model with other popular learning methods, namely Support Vector Machine [18] , Decision Tree [56] , and Random Forest [57] .…”

Section: Methodsmentioning

confidence: 99%

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections

Gupta

Jain

Taneja

et al. 2021

Applied Soft Computing

View full text Add to dashboard Cite

“…Thakur and Kumar in [41] also developed a hybrid financial trading support system by exploiting multi-category classifiers and random forest (RAF). They conducted their research on stock indices from NASDAQ, DOW JONES, S&P 500, NIFTY 50, and NIFTY BANK.…”

Section: Survey Of Related Workmentioning

confidence: 99%

Short-term stock market price trend prediction using a comprehensive deep learning system

2020

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Stock market is one of the major fields that investors are dedicated to, thus stock market price trend prediction is always a hot topic for researchers from both financial and technical domains. In this research, our objective is to build a state-of-art prediction model for price trend prediction, which focuses on short-term price trend prediction. As concluded by Fama in [26], financial time series prediction is known to be a notoriously difficult task due to the generally accepted, semi-strong form of market efficiency and the high level of noise. Back in 2003, Wang et al. in [44] already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. One of the key findings by them was that the volume was not found to be effective in improving the forecasting performance on the datasets they used, which was S&P 500 and DJI. Ince and Trafalis in [15] targeted short-term forecasting and applied support vector machine (SVM) model on the stock price prediction. Their main contribution is performing a comparison between multi-layer perceptron (MLP) and SVM then found that most of the scenarios SVM outperformed MLP, while

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“…Where N tree is the number of trees in RF model, and Err i and Err * i are the errors of each tree applied on OOB and perturbed OOB data, respectively. More details can be found in Thakur and Kumar [33]. The benefit of using RF is to have a reduced variance in comparison to a single tree so that over-fitting will not happen [30].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

Hariri-Ardebili

Barak

2020

Engineering Structures

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Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in riskinformed condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive, it is valuable to develop a series of forecasting models based on the unique ground motion characteristics. This paper discusses the application of six different machine learning techniques on forecasting the structural behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from ground motion signals and used in machine learning. A large set of about 2,000 real ground motions are used, each includes about 35 meta-features. The major outcome of this study is to show the applicability of meta-modeling-based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset. This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infrastructural systems.

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A hybrid financial trading support system using multi-category classifiers and random forest

Cited by 52 publications

References 32 publications

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections

Short-term stock market price trend prediction using a comprehensive deep learning system

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

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