scholarly journals Research on stock trend prediction method based on optimized random forest

2021 ◽  
Author(s):  
Lili Yin ◽  
Benling Li ◽  
Peng Li ◽  
Rubo Zhang
Keyword(s):  
Random Forest ◽  
Prediction Method ◽  
Trend Prediction

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms

2021 ◽  
Author(s):  
Mohamed G. El-Shafiey ◽  
Ahmed Hagag ◽  
El-Sayed A. El-Dahshan ◽  
Manal A. Ismail
Keyword(s):  
Genetic Algorithms ◽  
Heart Disease ◽  
Random Forest ◽  
Prediction Method ◽  
Disease Prediction

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

2022 ◽  
Vol 64 (1) ◽  
pp. 38-44
Author(s):  
Maosheng Gao ◽  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Yan Yu
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Time Warping ◽  
Trend Prediction ◽  
Residual Signal ◽  
Dynamic Time

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

scholarly journals System load trend prediction method based on IF-EMD-LSTM

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986765 ◽  
Author(s):  
Jing Yu ◽  
Feng Ding ◽  
Chenghao Guo ◽  
Yabin Wang
Keyword(s):  
Information System ◽  
Short Term Memory ◽  
Prediction Method ◽  
Short Term ◽  
Trend Prediction ◽  
Term Memory ◽  
System Load ◽  
Mode Function ◽  
Abnormal Points

Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. Each intrinsic mode function (IMF) and residual is predicted using a separate long-term and short-term memory neural network, and the predicted values are reconstructed from each long-term and short-term memory model. Finally, experimental verification was carried out on Amazon’s public data set and compared with autoregressive integrated moving average and Prophet models. The experimental results show the superior performance of the proposed IF-EMD-LSTM prediction model in information system load trend prediction.

Sign in / Sign up

Export Citation Format

Share Document