A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

2010 ◽  
Vol 21 (3) ◽  
pp. 595-610 ◽  
Author(s):  
J. P. Florido ◽  
H. Pomares ◽  
I. Rojas ◽  
J. M. Urquiza ◽  
M. A. Lopez-Gordo
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Deterministic Model ◽  
Time Series Forecasting ◽  
Selection Scheme

scholarly journals On Model Selection for Scalable Time Series Forecasting in Transport Networks

2021 ◽  
pp. 1-10
Author(s):  
Julien Monteil ◽  
Anton Dekusar ◽  
Claudio Gambella ◽  
Yassine Lassoued ◽  
Martin Mevissen
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Forecasting ◽  
Transport Networks ◽  
Selection For

Predictive Minimum Description Length Criterion for Time Series Modeling with Neural Networks

1996 ◽  
Vol 8 (3) ◽  
pp. 583-593 ◽  
Author(s):  
Mikko Lehtokangas ◽  
Jukka Saarinen ◽  
Pentti Huuhtanen ◽  
Kimmo Kaski
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Cross Validation ◽  
Minimum Description Length ◽  
Model Complexity ◽  
Time Series Modeling ◽  
Selection Scheme ◽  
The Neural Network ◽  
Multilayer Perceptron Network ◽  
Validation Technique

Nonlinear time series modeling with a multilayer perceptron network is presented. An important aspect of this modeling is the model selection, i.e., the problem of determining the size as well as the complexity of the model. To overcome this problem we apply the predictive minimum description length (PMDL) principle as a minimization criterion. In the neural network scheme it means minimizing the number of input and hidden units. Three time series modeling experiments are used to examine the usefulness of the PMDL model selection scheme. A comparison with the widely used cross-validation technique is also presented. In our experiments the PMDL scheme and the cross-validation scheme yield similar results in terms of model complexity. However, the PMDL method was found to be two times faster to compute. This is significant improvement since model selection in general is very time consuming.

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