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To improve the prediction accuracy of short-term load series, this paper proposes a hybrid model based on a multi-trait-driven methodology and secondary decomposition. In detail, four steps were performed sequentially, i.e., data decomposition, secondary decomposition, individual prediction, and ensemble output, all of which were designed based on a multi-trait-driven methodology. In particular, the multi-period identification method and the judgment basis of secondary decomposition were designed to assist the construction of the hybrid model. In the numerical experiment, the short-term load data with 15 min intervals was collected as the research object. By analyzing the results of multi-step-ahead forecasting and the Diebold–Mariano (DM) test, the proposed hybrid model was proven to outperform all benchmark models, which can be regarded as an effective solution for short-term load forecasting.

In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

Crude oil price forecasting is one of the most important topics in the field of energy research. Accordingly, numerous methods such as statistical, econometrical and intelligent approaches are applied for crude oil price forecasting. In this paper, a typical competitive learning algorithm, support vector machine (SVM), is empirically investigated to verify the feasibility and potentiality of SVM in crude oil price forecasting. For this purpose, five different prediction models, feed-forward neural networks (FNN), auto-regressive integrated moving average (ARIMA) model, fractional integrated ARIMA (ARFIMA) model, Markov-switching ARFIMA (MS-ARFIMA) model, and random walk (RW) model are used in the study. Experimental results obtained show that the SVM model outperforms the other five methods, implying that it is a fairly good candidate for crude oil price forecasting in terms of either one-step prediction or multi-step prediction.

To solve the high-dimensional issue in credit risk assessment, a hybrid clustering and boosting tree feature selection method is proposed. In the hybrid methodology, an  improved minimum spanning tree model is first used to remove redundant and irrelevant  features. Then three embedded feature selection approaches (i.e., Random Forest, XGBoost,  and AdaBoost) are used to further enhance the feature-ranking efficiency and obtain better  prediction performance by applying the optimal features. For verification purpose, two real-world credit datasets are used to demonstrate the effectiveness of the proposed hybrid clustering and boosting tree feature selection (CBTFS) methodology. Experimental results demonstrated that the proposed method is superior to others classic feature selection methods. This indicates that the proposed hybrid clustering and boosting tree feature selection method can be used as a promising tool for solving high-dimensional issue in credit risk assessment. First published online 12 February 2025

PurposeThe purpose of this paper is to provide new insights for managing knowledge reuse in terms of the duality of innovator personality. Continuously developing new products is crucial for firms to maintain and enhance their competitive advantages. However, the limited and highly specialized knowledge can cause innovators of firms to face difficulties in the process of new product development (NPD). In this setting, knowledge reuse becomes a solution that may benefit innovators to overcome the innovation dilemma. Given the fact that innovators with different personality are likely to form incongruent cognitions and affection on knowledge reuse, thus subsequently affecting the performance of NPD, there is an urgent need to investigate the effects of innovator personality in the entire process of knowledge reuse.Design/methodology/approachThis paper exploits five-factor model (FFM) of personality to comprehensively investigate the dual effects of innovator personality in managing knowledge reuse based on the two distinct sets of knowledge reuse initiation and implementation.FindingsBy using the data from 981 innovators of knowledge-intensive firms in China, this study finds that the FFM traits of conscientiousness and agreeableness had opposing effects on initiation and implementation of knowledge reuse. While the FFM traits of emotional stability and openness to experience both positively affect the knowledge reuse initiation and implementation. Moreover, the FFM traits of extraversion benefit the shaping of knowledge reuse initiation whereas encumbering the implementation of knowledge reuse.Originality/valueFirst, this study reveals the different roles of cognitive and affective traits of personality in shaping knowledge reuse. Second, this study exposes the role of innovator personality in determining the performance effects of knowledge reuse implementation. Third, this study highlights the dual effects of innovator personality in managing knowledge reuse. This study offers evidence for arranging the innovators with appropriate FFM traits in various stages of knowledge reuse.

In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100 < feature dimensions < sample size, and feature dimensions ≥ sample size. In the second stage, some typical feature extraction methods are tested regarding the two categories of high dimensionality. In the final stage, four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits. For the purpose of illustration and verification, credit classification experiments are performed on two publicly available credit risk datasets, and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.

Machine learning methods are widely used to evaluate the risk of small- and medium-sized enterprises (SMEs) in supply chain finance (SCF). However, there may be problems with data scarcity, feature redundancy, and poor predictive performance. Additionally, data collected over a long time span may cause differences in the data distribution, and classic supervised learning methods may exhibit poor predictive abilities under such conditions. To address these issues, a domain-adaptation-based multistage ensemble learning paradigm (DAMEL) is proposed in this study to evaluate the credit risk of SMEs in SCF. In this methodology, a bagging resampling algorithm is first used to generate a dataset to address data scarcity. Subsequently, a random subspace is applied to integrate various features and reduce feature redundancy. Additionally, a domain adaptation approach is utilized to reduce the data distribution discrepancy in the cross-domain. Finally, dynamic model selection is developed to improve the generalization ability of the model in the fourth stage. A real-world credit dataset from the Chinese securities market was used to validate the effectiveness and feasibility of the multistage ensemble learning paradigm. The experimental results demonstrated that the proposed domain-adaptation-based multistage ensemble learning paradigm is superior to principal component analysis, joint distribution adaptation, random forest, and other ensemble and transfer learning methods. Moreover, dynamic model selection can improve the model generalization performance and prediction precision of minority samples. This can be considered a promising solution for evaluating the credit risk of SMEs in SCF for financial institutions.

In order to deeply analyze the internal and external influencing factors of mutton price fluctuation, a multi-scale analytical framework was proposed in regards to the fluctuation mechanism analysis of mutton price in the Xinjiang region of China. By combining data decomposition and correlation analysis, this paper investigated the relationship between different mode components and multiple influencing factors to explain the fluctuation characteristics of mutton price in Xinjiang. Through empirical analysis, it was found that the proposed analytical framework could effectively explore the fluctuation mechanism of influencing factors related to mutton price, and could provide the corresponding policy support for the local government to adjust mutton price in Xinjiang.

This paper proposes an approach called feature weighted confidence with support vector machine (FWC–SVM) to incorporate prior knowledge into SVM with sample confidence. First, we use prior features to express prior knowledge. Second, FWC–SVM is biased to assign larger weights for prior weights in the slope vector \\[ \\] than weights corresponding to non-prior features. Third, FWC–SVM employs an adaptive paradigm to update sample confidence and feature weights iteratively. We conduct extensive experiments to compare FWC–SVM with the state-of-the-art methods including standard SVM, WSVM, and WMSVM on an English dataset as Reuters-21578 text collection and a Chinese dataset as TanCorpV1.0 text collection. Experimental results demonstrate that in case of non-noisy data, FWC–SVM outperforms other methods when the retaining level is not larger than 0.8. In case of noisy data, FWC–SVM can produce better performance than WSVM on Reuters-21578 dataset when the retaining level is larger than 0.4 and on TanCorpV1.0 dataset when the retaining level is larger than 0.5. We also discuss the strength and weakness of the proposed FWC–SVM approach.