In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market tech...
The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market's non-stationary and volatile nature, driven by...
Mathematical biosciences and engineering : MBE
Apr 15, 2021
Forecast of stock prices can guide investors' investment decisions. Due to the high-dimensional and long-memory characteristics of stock data, it is difficult to predict. The fractional grey model with convolution (FGMC (1, m)) can be used to predict...
Journal of the American College of Radiology : JACR
Jan 1, 2020
Radiologists today are under increasing work pressure. We surveyed radiologists in the United States across practice settings, and the overwhelming majority reported an increased workload. Artificial intelligence (AI), which includes machine learning...
Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. I contrast human and machine strengths and weaknesses in making investment decisions. The analysis rev...
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