An ensemble of single multiplicative neuron models for probabilistic prediction

U. Yolcu, Yaochu Jin, E. Eğrioğlu
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引用次数: 8

Abstract

Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real life problems, neural networks have interpretability issues with regard to their outputs. For example, the outputs of neural networks that are difficult to interpret compared to statistical inference systems' outputs that involve a confidence interval and probabilities about possible values of predictions on top of the point estimations. In this study, an ensemble of single multiplicative neuron models based on bootstrap technique has been proposed to get probabilistic predictions. The main difference of the proposed ensemble model compared to conventional neural network models is that it is capable of getting a bootstrap confidence interval and probabilities of predictions. The performance of the proposed model is demonstrated on different time series. The obtained results show that the proposed ensemble model has a superior prediction performance in addition to having outputs that are more interpretable and applicable to probabilistic evaluations than conventional neural networks.
用于概率预测的单个乘法神经元模型的集合
推理系统基本上旨在提供和呈现决策者可以在其决策过程中利用的知识(输出)。目前最常用的时间序列预测推理系统之一是基于人工神经网络的计算推理系统。尽管神经网络具有处理不确定性的能力,并且能够解决现实生活中的问题,但它们的输出存在可解释性问题。例如,与统计推理系统的输出相比,神经网络的输出难以解释,统计推理系统的输出涉及点估计之上的预测可能值的置信区间和概率。在本研究中,提出了一种基于自举技术的单乘法神经元模型集合来获得概率预测。与传统神经网络模型相比,该集成模型的主要区别在于它能够获得自举置信区间和预测概率。在不同的时间序列上验证了该模型的性能。结果表明,与传统神经网络相比,该集成模型具有更强的可解释性和更适用于概率评估的输出,并且具有更好的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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