Selecting Neural Network Forecasting Models Using the Zoomed-Ranking Approach

Santos, P. V.; Ludermir, Teresa B.; Prudêncio, Ricardo B. C.

doi:10.1109/sbrn.2008.31

Cited by 1 publication

(1 citation statement)

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“…Although, as a criterion for the selection of the best model, the minimization of some error function is often used, such as mean square error (MSE), absolute average deviation (MAD), cost functions [51], or even expert knowledge [52], because the performance of each measure is not the same, since they can favor or penalize certain characteristics in the data, and that, in the case of expert knowledge is not always easy to acquire; approaches based on the use of machine learning [53,54] and meta-learning [55][56][57][58][59] have been reported in the literature, which show advantages by allowing an automatic process of model selection based on the parallel evaluation of multiple network architectures, but they are limited to the execution of certain architectures and their implementation is complex. Other studies related to the topic include Qi and Zhang [43] who investigate the well-known criteria of AIC [60], BIC [61], square root of the mean square error (RMSE), absolute average percentage deviation (MAPE), and direction of occurrence (DA).…”

Section: Difficulties In the Prediction Of Time Series With Neural Nementioning

confidence: 99%

Encountered Problems of Time Series with Neural Networks: Models and Architectures

Sánchez-Sánchez¹,

García-González²,

Coronell³

2020

Recent Trends in Artificial Neural Networks - From Training to Prediction

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The growing interest in the development of forecasting applications with neural networks is denoted by the publication of more than 10,000 research articles present in the literature. However, the high number of factors included in the configuration of the network, the training process, validation and forecasting, and the sample of data, which must be determined in order to achieve an adequate network model for forecasting, converts neural networks in an unstable technique, given that any change in training or in some parameter produces great changes in the prediction. In this chapter, an analysis of the problematic around the factors that affect the construction of the neural network models is made and that often present inconsistent results, and the fields that require additional research are highlighted.

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