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

•A new neutrosophic set based time series forecasting model is proposed.•A quantum optimization algorithm is used to improve the accuracy.•The proposed model is compared with various existing models.•The proposed model is found to be very efficient. This article acquaints a new method to forecast th...

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Vydáno v:Computers in industry Ročník 111; s. 121 - 139
Hlavní autoři: Singh, Pritpal, Huang, Yo-Ping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.10.2019
Témata:
Entropy
Neutrosophic set
Quantum optimization algorithm (QOA)
Time series forecasting
Time series forecasting
Quantum optimization algorithm (QOA)
Neutrosophic set
Entropy
ISSN:0166-3615, 1872-6194
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Shrnutí:•A new neutrosophic set based time series forecasting model is proposed.•A quantum optimization algorithm is used to improve the accuracy.•The proposed model is compared with various existing models.•The proposed model is found to be very efficient. 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.
ISSN:0166-3615
1872-6194
DOI:10.1016/j.compind.2019.06.004