A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Wu, Dingming; Wang, Xiaolong; Wu, Shaocong

doi:10.3390/e23040440

Cited by 33 publications

(18 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Stock market data contain noise that affects forecast results. Methods, such as wavelet denoising [ 27 ] and principal component analysis [ 28 ], can eliminate the influence of irrelevant factors and improve the prediction effect to a certain extent. Time series analysis has been applied in fields, such as natural science [ 29 ] and industrial time series prediction [ 30 ].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

et al. 2022

Entropy

View full text Add to dashboard Cite

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Extreme learning machine (ELM) is one of the practical training algorithms for single-layer feedforward neural networks (Qiu et al, 2015). ELM has a faster training and better generalization performance than traditional machine learning algorithms and could overcome issues such as the local minimum, inappropriate learning rate, and overfitting (Wu et al, 2021). Therefore, it is widely used in the condition of classification and regression.…”

Section: Classification Modelsmentioning

confidence: 99%

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

et al. 2022

View full text Add to dashboard Cite

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

show abstract

“…It refers to information about the stock during the specific trading cycle of a stock exchange; such recorded information in its crude structure incorporates the starting and ending prices, the most noteworthy and least costs achieved, and the complete number of exchanged stocks, i.e., volume, for the specified period of trading. Several machine learning techniques have been applied to predict stock price movement [11][12][13]. In order to obtain appropriate predictions, this materialistic stock data have been merged with computational intellectual-based procedures [14][15][16] and different econometrics-based factual strategies [17][18][19].…”

Section: Related Workmentioning

confidence: 99%

“…Mean Absolute Error (MAE) measures the absolute average error between the real data and predicted data and is calculated using the Equation (11).…”

Section: Model Validationmentioning

confidence: 99%

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Dey¹,

Hossain²,

Hossain

et al. 2021

Algorithms

View full text Add to dashboard Cite

Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.

show abstract

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Cited by 33 publications

References 97 publications

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Contact Info

Product

Resources

About