RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention

Zheng, Haibin; Zhou, Zhiqiang; Chen, Jianyong

doi:10.1155/2021/8865816

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…The corresponding best CV score of each best combination is also presented in the table. The largest range of noise intensity [21,25] does not exist in all the best combinations, and only three of eight best combinations include the second largest range of noise intensity [16,20]. The largest ratio of inserted noisy observations "50%" occurs in only one best combination, and the largest ratio of inserted noisy features "70%" exists in three of eight best combinations.…”

Section: Performance Comparisons Between Gn-dafc-dkbag and Other Ense...mentioning

confidence: 99%

“…The number of observations to draw from the training data to train each base estimator (size of each bootstrap sample) is equal to 25%, 50%, or 100% of the training data size. Range of noise intensity { [1,5], [6,10], [11,15], [16,20], [21,25] } Ratio of inserted noisy observations 𝑝𝑝 { 10%, 30%, 50% } Ratio of inserted noisy features 𝑞𝑞 { 30%, 70% }…”

Section: Experiments Setupmentioning

confidence: 99%

“…Besides that, the noise change each data point, so a learning method encounter difficulties in fitting individual data points precisely; thus it will reduce over-fitting. A number of previous studies also utilized noise to avoid the over-fitting issue in machine learning methods [16] [17] [18]. Regards to the PHM field, it has been showed that noise utilization can enhance the faults detection performance for machinery systems [19] [20] [21] [22].…”

Section: Introduction 11 Related Workmentioning

confidence: 99%

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Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

2023

KSII TIIS

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Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

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Section: Performance Comparisons Between Gn-dafc-dkbag and Other Ense...mentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

Section: Introduction 11 Related Workmentioning

confidence: 99%

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Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

2023

KSII TIIS

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“…Lv et al [15] designed a LightGBM-optimized LSTM model to combine multitask learning with LSTM networks for stock market forecasting. Zheng et al [16] used a series of uniformly distributed random data based on an LSTM model to predict stock market indices. Gao et al [17] innovatively integrated multiple technical indicators, including financial data, to compare the stock market prediction performance between LSTM and GRU under different parameters.…”

Section: Related Workmentioning

confidence: 99%

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

Liu

Qin

You

et al. 2022

Computational Intelligence and Neuroscience

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The long short-term memory (LSTM) network is especially suitable for dealing with time series-related problems, which has led to a wide range of applications in analyzing stock market quotations and predicting future price trends. However, the selection of hyperparameters in LSTM networks was often based on subjective experience and existing research. The inability to determine the optimal values of the parameters results in a reduced generalization capability of the model. Therefore, we proposed a sparrow search algorithm-optimized LSTM (SSA-LSTM) model for stock trend prediction. The SSA was used to find the optimal hyperparameters of the LSTM model to adapt the features of the data to the structure of the model, so as to construct a highly accurate stock trend prediction model. With the Shanghai Composite Index stock data in the last decade, the mean absolute percentage error, root mean square error, mean absolute error, and coefficient of determination between stock prices predicted by the SSA-LSTM method and actual prices are 0.0093, 41.9505, 30.5300, and 0.9754. The result indicates that the proposed model possesses higher forecasting precision than other traditional stock forecasting methods and enhances the interpretability of the network model structure and parameters.

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“…The majority of the reviewed studies applied regularization approaches to prevent overfitting [23,25,83]. A few recent studies applied the procedure of data augmentation to prevent overfitting [154,155].…”

Section: Overfittingmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

et al. 2021

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With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

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RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention

Cited by 9 publications

References 23 publications

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

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