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

ABSTRACT: 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, uniqu

SUBMITTER: Wu D 

PROVIDER: S-EPMC8070264 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature