International crude oil prices are one of the important indicators in the global economy. Forecasting on crude oil prices can provide a predictive perspective for financial investment and development decision. This study explores the application of functional data analysis (FDA) techniques in the realm of crude oil price prediction, incorporating derivative information, and mixed-frequency data. The inclusion of derivative information from price trajectories is a key aspect of this study. It enriches the modeling process, offering valuable insights into rate-of-change and volatility patterns, ultimately improving predictive accuracy. In addition, the incorporation of mixed-frequency data, spanning diverse economic indicators and their respective time series, enhances the predictive accuracy of the forecasting model. To achieve a robust and interpretable decomposition of the crude oil price signal, a multivariate empirical mode decomposition (MEMD) approach is introduced. Subsequently, employing the adaptive neural fuzzy inference system to forecast submodes and aggregate them yields the ultimate prediction outcome. Empirical validation is conducted using historical Brent crude oil price datasets and robustness testing is performed using west texas intermediate (WTI) oil price data. Comparative analyses with conventional time series prediction models reveal the superiority of the proposed approach in capturing intricate temporal dynamics, irregular patterns, and abrupt changes.

In most existing linguistic distribution multiple criteria group decision-making (MCGDM) models, the sample size of group linguistic distribution assessment (LDA) is often neglected, and the decision makers are viewed to be independent. The probabilistic distribution is closely related to the sample size, and there exist more or fewer interference effects among different individual opinions. Thus, this article develops a quantum framework for modeling interference effects in the linguistic distribution MCGDM process. First, considering the sample size information, we redefine the LDAs and provide a new computational model for them. Second, to integrate a series of LDAs, the linguistic distribution weighted averaging (LDWA) operator is presented. Mathematic proofs reveal that the new LDWA operator can not only ensure the information integrality but also can avoid the conjunctive and disjunctive operations. Third, to explore the interference effects among individual opinions, a quantum framework is constructed. In this process, individual opinions are viewed as various wave functions occurring synchronously. They interfere with each and influence the aggregation result. Finally, an illustrative example of Internet finance soft power evaluation is provided to verify the effectiveness. Sensitivity and comparative analyses are also implemented to assess the stability and validity of our method.

In this study, the concept of linguistic Z-number fuzzy soft set ( L Z n F S S) is proposed to describe multiple uncertainties in practical decision making problems. L Z n F S S combines the concepts of fuzzy soft set, linguistic Z-number, and soft set, which could reflect both of the uncertainty in structure and the uncertainty in detailed evaluations. As an initial idea, the set operations on L Z n F S S s are put forward, the properties of such operations are also discussed. With traditional soft set based decision procedure and fuzzy soft set based decision procedure, a novel linguistic Z-number fuzzy soft set based group decision procedure is developed to solve multiattribute group decision making with linguistic Z-numbers. Wherein an extended technique for order preference by similarity to ideal solution is also developed. Finally, a numerical example is shown to illustrate the practicality and effectiveness of the given method.

Hesitant fuzzy linguistic preference relation (HFLPR) is a new preference structure that the decision makers (DMs) are hesitant about several possible linguistic terms of preference information for pairwise comparison between alternatives. This paper examines the additive consistency of HFLPR with a new expansion principle for hesitant fuzzy linguistic term sets (HFLTSs). In order to normalize HFLTSs with different numbers of linguistic terms, a least common multiple expansion (LCME) principle is proposed. According to the LCME principle, the additive consistent index of an HFLPR is defined to measure the consistency level of the HFLPR. For improving the unacceptable additive consistency of an HFLPR, a pure integer programming model is constructed to derive an acceptable additive consistent HFLPR. In group decision making (GDM) with HFLPRs, the similarities between DMs are calculated based on their individual HFLPR with acceptable additive consistency. Subsequently, the confidence degrees of DMs are defined to derive DMs’ weights, and some examples including an investment project management problem are analyzed to verify the effectiveness of the proposed method.

We develop a novel computation model of unbalanced linguistic variables on the basis of Archimedean copulas and corresponding co-copulas, which provides a new tool to aggregate unbalanced linguistic information. The properties of the proposed computational model are also studied. We present the concepts of weighted unbalanced Archimedean copula arithmetic aggregation operators and weighted unbalanced Archimedean copula geometric aggregation operators. The properties of these aggregation operators are further investigated. Finally, a group decision making of sensory evaluation is introduced to illustrate the feasibility and validity of our proposed computational model.

The aim of this paper is to develop some closed algebra operational laws for interval linguistic labels based on extended t-norms and s-norms. We discuss the properties of these operational laws, such as commutative law, associative law, and distribution law. Different kinds of extended t-norms and s-norms, such as the extended algebraic, extended Einstein, extended Hamacher, and extended Frank t-norms and s-norms, have been investigated to produce different operational laws. As an application of such operational laws, we propose some extended t-norms and s-norms based interval linguistic weighted power average operators. We also study some basic properties of such aggregation functions. The cross-entropy of interval linguistic information is proposed and applied to obtain the weights of attributes. An approach to multiple attributes decision making with interval linguistic information is proposed. Finally, two cases of practical decision issues are illustrated to show the application of the proposed method.

In this paper, we investigate the multiple attributes group decision making (MAGDM) problem with 2-tuple linguistic information. According to some closed operational laws of 2-tuple linguistic, some Algebra t-norm and s-norm based Heronian aggregation operators of 2-tuple linguistic information are put forward, the desired properties and the special cases where the parameters take different values are also discussed. Furthermore, a method of MAGDM under 2-tuple linguistic environment is proposed based on the Algebra t-norm and s-norm based 2-tuple linguistic Heronian mean operator or the Algebra t-norm and s-norm based 2-tuple linguistic weighted Heronian mean operator. Finally, a numerical example is presented to demonstrate the proposed method.

In this paper, we develop the generalized linguistic weighted logarithm averaging (GLWLA) operator and the generalized linguistic ordered weighted logarithm averaging (GLOWLA) operator in the group decision making under the linguistic surrounding. Then some properties of the families of the GLOWLA operator by different weighting vector are investigated. Furthermore, we present the generalized linguistic ordered weighted hybrid logarithm averaging (GLOWHLA) operator, which extends the GLOWLA operator. We also construct a nonlinear goal programming model to determine GLOWHLA weights from observational linguistic variable values under partial weight information. Finally, a numerical example is given to illustrate the new approach to evaluating university faculty for tenure and promotion, which indicates the feasibility and effectiveness of the new approach.

We develop some new cases of the induced continuous ordered weighted averaging (ICOWA) operator and study their desirable properties, which are very suitable to deal with group decision making (GDM) with interval fuzzy preference relations. First, we present the consensus indicator ICOWA (CI-ICOWA) operator which uses the consensus indicator of the interval fuzzy preference as the order inducing variable in the ICOWA operator. Then the concept of compatibility degree (CD) for two interval fuzzy preference relations is defined based on the continuous ordered weighted averaging (COWA) operator and the compatibility degree ICOWA (CD-ICOWA) operator is proposed which uses the CD as the order inducing variable in the ICOWA operator. Next, we investigate some desirable properties of the CD-ICOWA operator. Additionally, we construct an optimization model to obtain the weights of experts by minimizing the compatibility degree in the GDM. Finally, an illustrative numerical example is used to verify the developed approaches.