Advantages of direct input-to-output connections in neural networks: the Elman network for stock index forecasting

DOI10.1016/j.ins.2020.09.031zbMath1479.91385OpenAlexW3090382853MaRDI QIDQ2056356

Yanyan Li

Publication date: 2 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.09.031

zbMATH Keywords

Elman neural networkstock index forecastingdirect input-to-output connections (DIOCs)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Financial markets (91G15)

Related Items (3)

AE-DIL: a double incremental learning algorithm for non-stationary time series prediction via adaptive ensemble ⋮ Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression ⋮ Stock price forecasting based on Hausdorff fractional grey model with convolution and neural network

Cites Work

• An angle dominance criterion for evolutionary many-objective optimization
• Multisensor data fusion using Elman neural networks
• Data mining with computational intelligence.
• Low dimensional mid-term chaotic time series prediction by delay parameterized method
• Unnamed Item

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