Stock price forecasting based on Hausdorff fractional grey model with convolution and neural network

DOI10.3934/mbe.2021166zbMath1471.91533OpenAlexW3156147881MaRDI QIDQ1984060

Chunna Zhao, Wenhua Dong

Publication date: 13 September 2021

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021166

zbMATH Keywords

BP neural networkstock price forecastingFGMC (1, m)Hausdorff fractional derivativeLLE algorithmSewton-Cotes formula

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Fractional derivatives and integrals (26A33) Financial applications of other theories (91G80) Financial markets (91G15)

Cites Work

• Unnamed Item
• Multi-variable grey model based on dynamic background algorithm for forecasting the interval sequence
• A research on the grey prediction model GM(1,n)
• Application of a novel nonlinear multivariate grey Bernoulli model to predict the tourist income of China
• A hybrid model combining variational mode decomposition and an attention-GRU network for stock price index forecasting
• Advantages of direct input-to-output connections in neural networks: the Elman network for stock index forecasting
• Continuous grey model with conformable fractional derivative
• A novel grey Bernoulli model for short-term natural gas consumption forecasting
• The kernel-based nonlinear multivariate grey model
• The novel fractional discrete multivariate grey system model and its applications
• A prediction method using the grey model GMC(1,\(n\)) combined with the grey relational analysis: a case study on internet access population forecast

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