FD-LSTM: A Fuzzy LSTM Model for Chaotic Time-Series Prediction-Reference-Cited by-同舟云学术

FD-LSTM: A Fuzzy LSTM Model for Chaotic Time-Series Prediction

Published:2022-07-01 Issue:4 Volume:37 Page:70-78
ISSN:1541-1672
Container-title:IEEE Intelligent Systems
language:
Short-container-title:IEEE Intell. Syst.

Author:

Langeroudi Milad Keshtkar¹^ORCID,Yamaghani Mohammad Reza¹,Khodaparast Siavash¹

Affiliation:

1. Islamic Azad University, Lahijan, Iran

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/9670/9896783/09788030.pdf?arnumber=9788030

Reference12 articles.

1. Sports result prediction based on machine learning and computational intelligence approaches: A survey;langaroudi;Journal of Advances in Computer Engineering and Technology,2019

2. A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

3. Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections

4. A New Approach for Time Series Prediction Using Ensembles of IT2FNN Models with Optimization of Fuzzy Integrators

5. Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates

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