scholarly journals Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 158025-158034 ◽  
Author(s):  
Ruijian Liu ◽  
Yuhan Wang ◽  
Hong Zhou ◽  
Zeqiang Qian
Keyword(s):  
Wavelet Transform ◽  
Extreme Learning Machine ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

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

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 440
Author(s):  
Dingming Wu ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Wavelet Transform ◽  
Extreme Learning Machine ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Discrete Wavelet ◽  
Training Algorithms ◽  
Short Term ◽  
Trend Prediction ◽  
Main Challenge ◽  
Learning Machine

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

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