Prediction of stock market index based on ISSA-BP neural network

Liu, Xin; Guo, Junhong; Wang, Hua; Zhang, Fan

doi:10.1016/j.eswa.2022.117604

Cited by 53 publications

(14 citation statements)

References 38 publications

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“…As shown in Figure b–g, Comparing the predicted results of the two-layer BP neural network established in this work with the actual results, It can be observed that the Mean squared error of the prediction error is less than 10 –1 and the correlation coefficient R = 0.99211, indicating a strong agreement between the training results and the actual measurements. , Furthermore, when the trained neural network is applied for prediction, the predicted values closely match the actual measured results, affirming the guiding role of the constructed neural network in subsequent practical work.…”

Section: Resultsmentioning

confidence: 99%

Inspired by Plant Transpiration: Fabrication of a Unique Micro–Nano-Structured Janus Evaporator Using Waste Cotton Fabric for Enhanced Efficiency and Salt Resistance

Xiao,

Chen,

et al. 2024

ACS Sustainable Chem. Eng.

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The development of human society has inevitably caused damage to the Earth's environment, particularly impacting the crucial water cycle. The urgent need to address the scarcity of freshwater resources faced by humanity is evident. Drawing inspiration from the phenomenon of plant transpiration, this study presents a cutting-edge approach that leverages abandoned cotton textiles to fabricate a novel Janus evaporator with distinctive micro−nanoarchitecture, aligning with eco-friendly principles, termed the MoS 2 @CNTs/CNC Janus evaporator (MCC evaporator). Crafted from its own underlying micronano multilayer structure with superhydrophilicity and the upper layer of photothermal material with superhydrophobicity, the Janus architecture of the evaporator showcases remarkable performance. Under solar irradiation, the evaporation rate of the MCC evaporator can reach 3.16 kg m −2 h −1 , even at a salinity of 20 wt %, while demonstrating continuous operation for 8 h. Moreover, the application of the MCC evaporator in a dual-driven evaporation system powered by solar energy and low-grade waste heat (LGWH) holds promising potential. The calculated performance from the mathematical model of the MCC evaporator closely aligns with the observed results, with a mean square error of less than 10 −1 , highlighting the accuracy of the prediction model. This predictive model holds significant potential for optimizing and forecasting the performance of evaporators.

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Section: Resultsmentioning

confidence: 99%

Inspired by Plant Transpiration: Fabrication of a Unique Micro–Nano-Structured Janus Evaporator Using Waste Cotton Fabric for Enhanced Efficiency and Salt Resistance

Xiao,

Chen,

et al. 2024

ACS Sustainable Chem. Eng.

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“…To sum up, the back propagation neural network algorithm is more accurate in predicting the results [10]. So, the results predicted by the test set are used to manually make a reasonable prediction of the closing price of Google stock in the following 15 days.…”

Section: Model-based Prediction Results and Analysismentioning

confidence: 99%

Google stock price prediction by the back propagation neural network

Sun

2023

TNS

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Due to the influence of internal and external factors, stock prices often fluctuate greatly, which is also a big difficulty in the field of data mining and machine learning. This paper uses linear regression model, BP neural network model, and hard voting model to forecast Google stock. The results and accuracy of these three prediction models were observed respectively. Then a more accurate and practical model was selected to make a reasonable prediction of the closing price of Google stock in the following 15 days. The data set used is divided into two subsets, with some data used to fit the model and the rest used to evaluate the model. The results prove that the BP neural network model has the best prediction effect. Therefore, this model was selected for the next step of prediction, and the conclusive prediction result proved that the closing price of Google stock would decline in the following 15 days.

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“…The results showed that the proposed model had superior predictive ability. In order to pay attention to long-term dependencies and short-term dependencies of stock series simultaneously, Liu et al [32] proposed to combine VMD, a self-attention LSTM and a self-attention TCN for stock price prediction. The experimental results showed that the proposed hybrid model had better robustness and generalization than other methods.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition

Li,

Chen,

Sun

et al. 2024

IEEE Access

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The stock market plays an increasingly important role in the global economy. Accurate stock price forecasting not only aids government in predicting economic trends, but also helps investors anticipate higher expected returns. Nevertheless, hurdles such as non-linearity, complexity and high volatility make it a daunting task to predict stock prices. To address this issue, this paper proposes a new hybrid model, termed Hierarchical Decomposition based Forecasting Model (HDFM), to decompose and forecast stock prices in a hierarchical fashion. The model utilises complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for the initial decomposition of stock price time series. To enhance the predictive efficiency, sub-series with similar sample entropy from the decomposition are combined with the K-means clustering method. Through thorough analysis, it is found that the first combined sub-series contains more high-frequency signals. Therefore, the first combined sub-series is subjected to a second decomposition with variational mode decomposition (VMD). Afterwards, the gated recurrent unit (GRU) model is used to predict each of the sub-series individually. The final results are obtained by merging the prediction outcomes. The proposed model has been evaluated on three different stock markets. The experimental results showed that the proposed model outperformed other forecasting methods across all the stock indices. Moreover, ablation studies demonstrated the effectiveness of each individual component within the proposed model.

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Prediction of stock market index based on ISSA-BP neural network

Cited by 53 publications

References 38 publications

Inspired by Plant Transpiration: Fabrication of a Unique Micro–Nano-Structured Janus Evaporator Using Waste Cotton Fabric for Enhanced Efficiency and Salt Resistance

Inspired by Plant Transpiration: Fabrication of a Unique Micro–Nano-Structured Janus Evaporator Using Waste Cotton Fabric for Enhanced Efficiency and Salt Resistance

Google stock price prediction by the back propagation neural network

Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition

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