On the use of hybrid models in wedge trust region

2013 ◽  
Author(s):  
Yue Yang ◽  
Fengxia Xu ◽  
Qinghua Zhou ◽  
Peipei Zhou
Keyword(s):  
Trust Region ◽  
Hybrid Models

Combination of End-to-End and Hybrid Models for Speech Recognition

2020 ◽  
Author(s):  
Jeremy H.M. Wong ◽  
Yashesh Gaur ◽  
Rui Zhao ◽  
Liang Lu ◽  
Eric Sun ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Hybrid Models ◽  
End To End

Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models

2021 ◽  
pp. 126373
Author(s):  
Yeditha Pavan Kumar ◽  
Rathinasamy Maheswaran ◽  
Ankit Agarwal ◽  
Bellie Sivakumar
Keyword(s):  
Daily Precipitation ◽  
Hybrid Models

Spatial Prediction of Landslides Along National Highway-6, Hoa Binh Province, Vietnam Using Novel Hybrid Models

2021 ◽  
pp. 1-22
Author(s):  
Ha Thi Hang ◽  
Hoang Tung ◽  
Pham Duy Hoa ◽  
Nguyen Viet Phuong ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Spatial Prediction ◽  
Hybrid Models ◽  
National Highway ◽  
Hoa Binh

scholarly journals Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Risk Measures ◽  
Hybrid Models ◽  
Stock Volatility ◽  
Management Actions ◽  
Equity Risk ◽  
Hedging Strategies ◽  
Volatility Models

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

Novel hybrid models for the design of upflow anaerobic filters separated in phases

2021 ◽  
Author(s):  
Julio Isaac Maldonado Maldonado ◽  
Adriana Mercedes Márquez Romance ◽  
Edilberto Guevara Pérez ◽  
Sergio Alejandro Pérez Pacheco ◽  
Demetrio José Rey Lago
Keyword(s):  
Hybrid Models
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