Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Zainuddin, Nurul Hila; Lola, Muhamad Safiih; Djauhari, Maman A.; Yusof, Fadhilah; Ramlee, Mohd Noor Afiq; Deraman, Aziz; Ibrahim, Yahaya; Abdullah, Mohd Tajuddin

doi:10.1016/j.asoc.2019.105676

Cited by 24 publications

(27 citation statements)

References 51 publications

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“…The artificial neural network model has also been applied to the simulate of European electricity load ( Behm et al, 2020 ) and wind power generation ( Zafirakis et al, 2019 ). In addition, combination models related to artificial neural networks are also common in practical applications ( Zainuddin et al, 2019 ).…”

Section: Literature Reviewsmentioning

confidence: 99%

Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China

Wang

Jiang

2021

Journal of Cleaner Production

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Section: Literature Reviewsmentioning

confidence: 99%

Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China

Wang

Jiang

2021

Journal of Cleaner Production

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“…There has been a huge amount of work done on the use of bootstrap and its theoretical limitations. The interested reader is referred to several general overviews [20][21][22][23][24][25][26][27][28][29][30][31][32], to the discussion on the evaluation of the confidence intervals [26,27,29,33,34], to the discussion on how to remove the iid hypothesis [26,35], and the application and limitations in various fields, from medicine to nuclear physics and geochemistry [5,[26][27][28][29][36][37][38][39][40][41]]. An analysis of the statistical theory on which the bootstrap method is based, goes beyond this tutorial and will not be covered here.…”

Section: Bootstrapmentioning

confidence: 99%

Estimating Neural Network’s Performance with Bootstrap: A Tutorial

Michelucci

Venturini

2021

MAKE

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Neural networks present characteristics where the results are strongly dependent on the training data, the weight initialisation, and the hyperparameters chosen. The determination of the distribution of a statistical estimator, as the Mean Squared Error (MSE) or the accuracy, is fundamental to evaluate the performance of a neural network model (NNM). For many machine learning models, as linear regression, it is possible to analytically obtain information as variance or confidence intervals on the results. Neural networks present the difficulty of not being analytically tractable due to their complexity. Therefore, it is impossible to easily estimate distributions of statistical estimators. When estimating the global performance of an NNM by estimating the MSE in a regression problem, for example, it is important to know the variance of the MSE. Bootstrap is one of the most important resampling techniques to estimate averages and variances, between other properties, of statistical estimators. In this tutorial, the application of resampling techniques (including bootstrap) to the evaluation of neural networks’ performance is explained from both a theoretical and practical point of view. The pseudo-code of the algorithms is provided to facilitate their implementation. Computational aspects, as the training time, are discussed, since resampling techniques always require simulations to be run many thousands of times and, therefore, are computationally intensive. A specific version of the bootstrap algorithm is presented that allows the estimation of the distribution of a statistical estimator when dealing with an NNM in a computationally effective way. Finally, algorithms are compared on both synthetically generated and real data to demonstrate their performance.

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“…In this way, the split of test and training data that gives the lowest error rate were successfully determined. We adopted MSE as a performance measurement technique in different studies based on ANN [ [113] , [114] , [115] , [116] , [117] ]. Then, the dataset is divided into training and test sets.…”

Section: A Real Case Application For Turkeymentioning

confidence: 99%

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Ayyıldız

Erdoğan

Gümüş

2021

Computers in Biology and Medicine

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Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Cited by 24 publications

References 51 publications

Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China

Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China

Estimating Neural Network’s Performance with Bootstrap: A Tutorial

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

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