Boosting the Accuracy of Stock Market Prediction using XGBoost and Long Short-Term Memory

Gumelar, Agustinus Bimo; Setyorini, Haryati; Adi, Derry Pramono; Nilowardono, Sengguruh; Latipah,; Widodo, Agung; Wibowo, Achmad Teguh; Sulistyono, MY Teguh; Christine, Evy

doi:10.1109/isemantic50169.2020.9234256

Cited by 18 publications

(4 citation statements)

References 22 publications

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“…In recent years, XGBoost has garnered significant attention, as shown in a diversity of studies and research, highlighted by its application in fields such as health diagnosis (Bao, 2020) (Budholiya et al, 2022), stock forecast (Gumelar et al, 2020) (Wang & Guo, 2020), and even career prediction. The overall computational complexity of the XGBoost algorithm is the combination of all the complexities mentioned above, with particular emphasis on the tree construction and the tree update steps.…”

Section: Xgboost Algorithmmentioning

confidence: 99%

Career path prediction using XGBoost Model and students’ academic results

Nguyen,

et al. 2023

CTU J. of Inn. & Sus. Dev.

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This paper proposes an approach for constructing a system for career prediction by applying the eXtreme Gradient Boosting (XGBoost) Decision Tree model to the academic results of Ho Chi Minh International University’s School of Computer Science and Engineering graduates in the past 5 years. Initially, the dataset is cleaned up and normalized to be usable for the prediction algorithm with the help of Python 3 programming language. It is then split into 2 subsets: one for training (80 percent) and the other for testing (20 percent). After that, the algorithm uses the training subset to build the classification model. Finally, the testing subset is loaded into the model to predict each student’s career path based on the respective inputs and hyper-parameters tuning is employed to boost the model’s accuracy. By utilizing this solution, the problem related to predicting students’ future career paths based on their performance throughout their years studying at the university can be adequately addressed and handled.

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Section: Xgboost Algorithmmentioning

confidence: 99%

Career path prediction using XGBoost Model and students’ academic results

Nguyen,

et al. 2023

CTU J. of Inn. & Sus. Dev.

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“…Furthermore, some researchers preferred light machine learning methods for addressing stock price prediction, such as the hybrid GA-XGBoost algorithm used in [41], time-series forecasting Autoregressive Moving Average (ARMA) and SARIMA in [42,43]; embedded principal component analysis, discrete wavelet transform [44] for XGBoost; the hybrid method based on XGBoost and Long Short-Term Memory [45] etc. XGBoost efficiently implements a tree-based gradient-boosting algorithm for supervised classification and regression (XGBRegressor) problems.…”

Section: Stock Price Data Predictionmentioning

confidence: 99%

A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

Snåšel

Silva

Pant

et al. 2023

Preprint

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“…Recent examples of gradient boosting machine applications for time series prediction can be seen in Johnson et al (2017) and Lopez‐Martin et al (2019); specifically for COVID‐19 data, this method was applied in Haimovich et al (2020) and Malki et al (2020). Specifically for financial problems, recent works include Gumelar et al (2020), which compared XGBoost with Long Short‐Term Memory (LSTM) neural networks for stock price prediction using data from 25 companies listed in the Indonesia Stock Exchange; Jabeur et al (2021), which compared two GBM variants (XGBoost and CatBoost) to forecast the time series of gold price data; and Severino and Peng (2021), which compared GBM with eight other machine learning models for the fraud detection task using real‐world microdata using eXplainable Artificial Intelligence (XAI) methods to evaluate the most relevant features at both global and local levels.…”

Section: Boosting Modelsmentioning

confidence: 99%

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

Albuquerque

Peng

Silva

2022

Journal of Forecasting

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This paper discusses the application of ensemble techniques for the prediction of time series, presenting an in‐depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches. We also discuss the theoretical foundations of the ensemble‐based models, presenting measures of model stability and the main aggregation methods to combine the forecasts of the individual models, as well as recommendations for future developments for related research agendas.

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Boosting the Accuracy of Stock Market Prediction using XGBoost and Long Short-Term Memory

Cited by 18 publications

References 22 publications

Career path prediction using XGBoost Model and students’ academic results

Career path prediction using XGBoost Model and students’ academic results

A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

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