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A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm

机译：一种基于中性学集和量子优化算法的新的混合时间序列预测模型

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This article acquaints a new method to forecast the time series dataset based on neutrosophic-quantum optimization approach. This study uses neutrosophic set (NS) theory to represent the inherited uncertainty of time series dataset with three different memberships as truth, indeterminacy and false. We refer such representations of time series dataset as neutrosophic time series (NTS). This NTS is further utilized for modeling and forecasting time series dataset. Study showed that the performance of NTS modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. To resolve this issue, this study selects quantum optimization algorithm (QOA) and ensembles with the NTS modeling approach. QOA improves the performance of the NTS modeling approach by selecting the globally optimal universe of discourse and its corresponding intervals from the list of local optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signify the efficiency of the proposed NTS-QOA model over existing benchmark models in terms of average forecasting error rates (AFERs) of 0.44%, 0.066% and 1.27% for the university enrollment, TAIFEX index and TSEC weighted index, respectively. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文熟悉基于中性学 - 量子优化方法预测时间序列数据集的新方法。本研究使用中性学套装（NS）理论来表示时间序列数据集的继承不确定性，其中三个不同的成员资格为真理，不确定性和错误。我们将时间序列数据集的这些表示指示为中性时间序列（NTS）。该NTS进一步用于建模和预测时间序列数据集。研究表明，NTS建模方法的性能高度依赖于话语宇宙的最佳选择及其相应的间隔。要解决此问题，本研究选择了与NTS建模方法的量子优化算法（QoA）和合奏。 Qoa通过从本地最佳解决方案列表中选择话语的全局最优宇宙来提高NTS建模方法的性能。拟议的混合模型（即，NTS-QOA模型）被验证和验证了Alabama（美国）的大学注册数据集，台湾期货交易所（TAIFEX）指数和台湾证券交易所公司（TSEC）加权指数。各种实验结果在平均预测误差率（上方）分别表示提出的NTS-QoA模型的效率分别为大学注册，TAIFEX指数和TSEC加权指数的0.44％，0.066％和1.27％。 （c）2019年Elsevier B.V.保留所有权利。

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