Financial Time Series Forecasting: Comparison of Traditional and Spiking Neural Networks-Reference-Cited by-同舟云学术

Financial Time Series Forecasting: Comparison of Traditional and Spiking Neural Networks

Published:2021 Issue: Volume:192 Page:5023-5029
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Mateńczuk Karolina,Kozina Agata,Markowska Aleksandra,Czerniachowska Kateryna,Kaczmarczyk Klaudia,Golec Paweł,Hernes Marcin,Lutosławski Krzysztof,Kozierkiewicz Adrianna,Pietranik Marcin,Rot Artur,Dyvak Mykola

Publisher

Elsevier BV

Subject

General Engineering

Reference22 articles.

1. “Spatiotemporal adaptive neural network for long-term forecasting of financial time series”;Chatigny;International Journal of Approximate Reasoning,2020

2. “A new financial data forecasting model using genetic algorithm and long short-term memory network”;Huang;Neurocomputing,2021

3. “Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model”;Aisyah Mohammed;Communications in Statistics - Theory and Methods,2020

4. “Forecasting Financial Returns Volatility: A GARCH-SVR Model”;S Sun;Computational Economics,2020

5. “Interval Forecasting of Financial Time Series by Accelerated Particle Swarm-Optimized Multi-Output Machine Learning System”;Chou;IEEE Access,2020

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