Stock price dynamics prediction based on multi-scale fractals and deep learning.

ABSTRACT: The complexity of stock price fluctuations stems from its multi-scale characteristics, nonlinear dynamic characteristics, and fractal structure. To better capture the fractal characteristics of stock prices, this paper creatively proposes a prediction method based on fractal feature extraction and deep learning. First, the generalized Hurst exponent Hq, high-order fractal dimension FDq, and multifractal spectrum MFSq are combined to characterize the long-range correlation and local complexity of stock price series from different scales. In addition, Rényi entropy and generalized fractional Brownian motion (GFBM) are introduced to enhance the descriptive ability of features. Secondly, a multi-scale fractal feature fusion mechanism (MSA) is designed to achieve feature aggregation in the time

SUBMITTER: Du Y 

PROVIDER: S-EPMC12671762 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature