A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks

1995 ◽  
Vol 13 (3) ◽  
pp. 265 ◽  
Author(s):  
Norman R. Swanson ◽  
Halbert White
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Selection ◽  
Term Structure ◽  
Linear Models ◽  
Model Selection Approach ◽  
Selection Approach ◽  
Artificial Neural

scholarly journals Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

2018 ◽  
Vol 10 (6) ◽  
pp. 939 ◽  
Author(s):  
Lea Beusch ◽  
Loris Foresti ◽  
Marco Gabella ◽  
Ulrich Hamann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Artificial Neural ◽  
Rainfall Retrieval

scholarly journals Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models

2021 ◽  
Vol 10 (5) ◽  
pp. 293
Author(s):  
Blerina Vika ◽  
Ilir Vika
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Smooth Transition ◽  
Parameter Method ◽  
Economic Time Series ◽  
Nonlinear Methods ◽  
Linear And Nonlinear ◽  
Artificial Neural

Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework.   Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021

A new model selection strategy in artificial neural networks

2008 ◽  
Vol 195 (2) ◽  
pp. 591-597 ◽  
Author(s):  
Erol Eğrioğlu ◽  
Çağdaş Hakan Aladağ ◽  
Süleyman Günay
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Selection ◽  
Selection Strategy ◽  
New Model ◽  
Artificial Neural

scholarly journals Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

2019 ◽  
Vol 31 (4) ◽  
pp. 377-386 ◽  
Author(s):  
Petar Andraši ◽  
Tomislav Radišić ◽  
Doris Novak ◽  
Biljana Juričić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Air Traffic ◽  
Traffic Situation ◽  
Network Configuration ◽  
Air Traffic Controllers ◽  
Complexity Estimation ◽  
Artificial Neural

Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).

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