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

Abstract

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.