With the explosive growth of multidimensional data, uncertainty in time series forecasting has become increasingly prominent. Although deep learning models demonstrate strong predictive capabilities, they remain sensitive to data uncertainty, while fuzzy logic systems (FLS) offer interpretability but often suffer from predefined parameter sensitivity and high computational overhead. To address these challenges, this paper proposes the IT2-FLS-BiRNN framework, which combines the temporal feature extraction capabilities of Bidirectional Recurrent Neural Networks (BiRNNs) with the uncertainty quantification strengths of Interval Type-2 FLS (IT2-FLS). To overcome limitations related to membership function sensitivity and constrained generalization, a fuzzy membership learning strategy is introduced to enable a data-aligned transformation of input features into IT2 fuzzy sets. Moreover, to alleviate the considerable computational overhead commonly associated with fuzzy systems, a parallelizable IT2-FLS inference mechanism is developed, which leverages matrix-based parallel computation for efficient reasoning. To evaluate the effectiveness and robustness of the IT2-FLS-BiRNN framework, experiments are conducted on ten publicly available benchmark datasets with varying levels of noise and nonlinear characteristics. Experimental results show that the proposed method consistently achieves superior performance across multiple evaluation metrics and outperforms most baseline models. In addition, noise analysis further reveals that IT2-FLS-BiRNN maintains significantly lower prediction errors compared to competing methods, particularly under high noise conditions, thereby highlighting its robustness in managing uncertainty for time series forecasting.

Several recent reviews summarize common missing value analysis methods. However, none of them provide a systematic and in-depth summary of the analytical challenges and solutions for dealing with missing values. For the purpose of guiding the handling of missing values, this review aims to consolidate current developments in novel missing-value research methodologies. In particular, we comprehensively investigated cutting-edge missing value solutions and methodically studied the main challenges associated with missing values analysis (missing mechanisms, missing patterns, and missing rates). Furthermore, we reviewed 63 publications that compare different strategies for deleting and imputing missing values. Then we investigated data characteristics, highlighted three main problems when analyzing missing values, and analyzed the performance of missing value solutions in these studied papers. Moreover, we conducted comprehensive experiments on 9 public datasets using typical missing value processing methods and provided a simple guided decision tree for handling missing values. Finally, we described current Research hotspots and open challenges, which give potential research topics.

In real cases, missing values tend to contain meaningful information that should be acquired or should be analyzed before the incomplete dataset is used for machine learning tasks. In this work, two algorithms named jointly fuzzy C-Means and vaguely quantified nearest neighbor (VQNN) imputation (JFCM-VQNNI) and jointly fuzzy C-Means and fitted VQNN imputation (JFCM-FVQNNI) have been proposed by considering clustering conception and sufficient extraction of uncertain information. In the proposed JFCM-VQNNI and JFCM-FVQNNI algorithm, the missing value is regarded as a decision feature, and then, the prediction is generated for the objects that contain at least one missing value. Specially, as for JFCM-VQNNI algorithm, indistinguishable matrixes, tolerance relations, and fuzzy membership relations are adopted to identify the potential closest filled values based on corresponding similar objects and related clusters. On the basis of JFCM-VQNNI algorithm, JFCM-FVQNNI algorithm synthetic analyzes the fuzzy membership of the dependent features for instances with each cluster. In order to fill the missing values more accurately, JFCM-FVQNNI algorithm performs fuzzy decision membership adjustment in each object with respect to the related clusters by considering highly relevant decision attributes. The experiments have been carried out on five datasets. Based on the analysis of root-mean-square error, mean absolute error, comparison of imputation values with actual values, and classification accuracy results analysis, we can draw the conclusion that the proposed JFCM-FVQNNI and JFCM-VQNNI algorithms yields sufficient and reasonable imputation performance results by comparing with fuzzy C-Means parameter-based imputation algorithm and fuzzy C-Means rough parameter-based imputation algorithm.

The existing approaches for attribute discrimination are applied to clinical data with unambiguous boundaries, and rarely take into careful consideration on how to utilize psychometric data with fuzziness. In addition, it is difficult for conventional attribute reduction methods to reduce attributes of psychometric data which are composed of a lot of attributes and contain a relatively small-scale samples. Importantly, these methods cannot be used to reduce options which are relevant to each other. In this paper, we first introduce new concepts, that is, option entropy and option influence degree, which are employed to describe the relation and distribution of options. Then, we propose a hybrid attribute discrimination method for psychometric data with fuzziness, called a hybrid attribute discrimination for psychometric data with fuzziness (ADPDF). ADPDF contains three essential techniques: 1) a fuzzy option reduction method, which aims to combine a fuzzy option to adjacent options, and is used to reduce the fuzziness of options in a psychometry and 2) k -fold attribute reduction method, which partitions all samples into several subsets and negotiates the reduction results of different subsets, and reduces the noise for the purpose of accurately discovering key attributes. In order to show the advantages of the proposed approach, we conducted experiments on two real datasets collected from clinical diagnoses. The experimental results show that the proposed method can decrease the correlation between options effectively. Interestingly, we find three reserved options and one hundred samples in each subset show the best classification performance. Finally, we compare the proposed method with typical attribute discrimination algorithms. The results reveal that our method can improve the classification accuracy with the guarantee of time performance.

This paper describes a new prediction method of tuning curves of a LC-tank voltage-controlled oscillator (VCO) with period calculation technique. With this period calculation technique, the prediction of oscillator tuning curves is more accurate compared with the traditional harmonic approximation. The theoretical analyses are experimentally validated with a CMOS complementary LC-tank VCO implemented in 0.35/spl mu/m 1P4M pure logic CMOS process.