Statistical Models and Artificial Neural Networks

Author(s):  
Gerhard Arminger ◽  
Daniel Enache
2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Akhter Mohiuddin Rather

Fractional This paper proposes a deep learning approach for prediction of nonstationary data. A new regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of the proposed model, however in this work stock data has been used due to the fact that stock data has a property of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some statistical models and artificial neural networks. Traditional statistical models did not yield any expected results; artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.


2018 ◽  
Vol 78 (4) ◽  
pp. 511-520 ◽  
Author(s):  
Ronaldo Oliveira dos Santos ◽  
Rubiene Neto-Soares ◽  
Mickelly Paula Queiroz Pimentel ◽  
Jadson Coelho de Abreu ◽  
Robson Borges de Lima ◽  
...  

2021 ◽  
Vol 25 ◽  
pp. 104274
Author(s):  
Azhari A. Elhag ◽  
Tahani A. Aloafi ◽  
Taghreed M. Jawa ◽  
Neveen Sayed-Ahmed ◽  
F.S. Bayones ◽  
...  

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