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Wei Lu

Bio: Wei Lu is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Fuzzy logic & Granular computing. The author has an hindex of 12, co-authored 42 publications receiving 622 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel method of partitioning the universe of discourse of time series based on interval information granules is proposed for improving forecasting accuracy of model and shows results that produce more reasonable intervals exhibiting sound semantics.

115 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel clustering model, in which probabilistic information granules of missing values are incorporated into the Fuzzy C-Means clustering of incomplete data by involving the maximum likelihood criterion.
Abstract: Missing values are a common phenomenon when dealing with real-world data sets. Analysis of incomplete data sets has become an active area of research. In this paper, we focus on the problem of clustering incomplete data, which is intended to introduce some prior distribution information of the missing values into the algorithm of fuzzy clustering. First, non-parametric hypothesis testing is employed to describe the missing values adhering to a certain Gaussian distribution as probabilistic information granules based on the nearest neighbors of incomplete data. Second, we propose a novel clustering model, in which probabilistic information granules of missing values are incorporated into the Fuzzy C-Means clustering of incomplete data by involving the maximum likelihood criterion. Third, the clustering model is optimized by using a tri-level alternating optimization utilizing the method of Lagrange multipliers. The convergence and the time complexity of the clustering algorithm are also discussed. The experiments reported both on synthetic and real-world data sets demonstrate that the proposed approach can effectively realize clustering of incomplete data.

95 citations

Journal ArticleDOI
TL;DR: A new modeling approach to realize interval prediction, in which the idea of information granules and granular computing is integrated with the classical Chen's method, is proposed, with a clear advantage of reducing computation overhead of modeling and simplifying forecasting.
Abstract: A lot of research has resulted in many time series models with high precision forecasting realized at the numerical level. However, in the real world, higher numerical precision may not be necessary for the perception, reasoning and decision-making of human. Model of time series with an ability of humans to perceive and process abstract entities (rather than numeric entities) is more adaptable for some problems of decision-making. With this regard, information granules and granular computing play a primordial role. Fox example, if change range (intervals) of stock prices for a certain period in the future is regarded as information granule, constructing model that can forecast change ranges (intervals) of stock prices for a period in the future is better able to help stock investors make reasonable decisions in comparison with those based upon specific forecasting numerical value of stock price. In this paper, we propose a new modeling approach to realize interval prediction, in which the idea of information granules and granular computing is integrated with the classical Chen's method. The proposed method is to segment an original numeric time series into a collection of time windows first, and then build fuzzy granules expressed as a certain fuzzy set over each time windows by exploiting the principle of justifiable granularity. Finally, fuzzy granular model can be constructed by mining fuzzy logical relationships of adjacent granules. The constructed model can carry out interval prediction by degranulation operation. Two benchmark time series are used to validate the feasibility and effectiveness of the proposed approach. The obtained results demonstrate the effectiveness of the approach. Besides, for modeling and prediction of large-scale time series, the proposed approach exhibit a clear advantage of reducing computation overhead of modeling and simplifying forecasting.

82 citations

Journal ArticleDOI
TL;DR: A novel modeling and prediction approach of time series based on synergy of high-order fuzzy cognitive map (HFCM) and fuzzy c-means clustering is proposed, in which fuzzy c's clustering algorithm is used to construct information granules, transform original time series into granular time series and generate a structure of HFCM prediction model in an automatic fashion.
Abstract: The time series prediction models based on fuzzy set theory have been widely applied to diverse fields such as enrollments, stocks, weather and etc., as they can handle prediction problem under uncertain circumstances in which data are incomplete or vague. Researchers have presented diverse approaches to support the development of fuzzy time series prediction models. While the existing approaches exhibit two evident shortcomings: one is that they have low efficiency of development, which is hardly applicable in the prediction problem involving large-scale time series, and the other is that fuzzy logical relationships mined in an ad hoc way cannot uncover the global characteristics of time series, which reduces accuracy of the resulting model. In this paper, a novel modeling and prediction approach of time series based on synergy of high-order fuzzy cognitive map (HFCM) and fuzzy c-means clustering is proposed, in which fuzzy c-means clustering algorithm is used to construct information granules, transform original time series into granular time series and generate a structure of HFCM prediction model in an automatic fashion. Subsequently depending on historical data of time series, the HFCM prediction model of time series is completely formed by exploiting PSO algorithm to learn all parameters of one. Finally, the developed HFCM prediction model can realize numeric prediction by performing inference in the granular space. Four benchmark time series data sets with different statistical characteristics coming from different areas are applied to validate the feasibility and effectiveness of the proposed modeling approach. The obtained results clearly show the effectiveness of the approach. The developed HFCM prediction models depend on historical data of time series and is emerged in the form of map, which is simpler, legible and have high-level interpretability. Additionally, the proposed approach also exhibits a clear ability to handle the prediction problem of large-scale time series.

76 citations

Journal ArticleDOI
TL;DR: This paper realizes the kernel clustering of incomplete data set by means of a gradient-based alternating optimization of interval data clustering based on the interval kernel distance.

46 citations


Cited by
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Journal ArticleDOI
TL;DR: A goal-driven overview of numerous theoretical developments recently reported in this area, and an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.
Abstract: Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.

170 citations

Journal ArticleDOI
TL;DR: This paper reviews the state of the art developments in deep learning for time series prediction and categorizes them into discriminative, generative, and hybrids models, based on modeling for the perspective of conditional or joint probability.
Abstract: In order to approximate the underlying process of temporal data, time series prediction has been a hot research topic for decades. Developing predictive models plays an important role in interpreting complex real-world elements. With the sharp increase in the quantity and dimensionality of data, new challenges, such as extracting deep features and recognizing deep latent patterns, have emerged, demanding novel approaches and effective solutions. Deep learning, composed of multiple processing layers to learn with multiple levels of abstraction, is, now, commonly deployed for overcoming the newly arisen difficulties. This paper reviews the state-of-the-art developments in deep learning for time series prediction. Based on modeling for the perspective of conditional or joint probability, we categorize them into discriminative, generative, and hybrids models. Experiments are implemented on both benchmarks and real-world data to elaborate the performance of the representative deep learning-based prediction methods. Finally, we conclude with comments on possible future perspectives and ongoing challenges with time series prediction.

142 citations

Journal ArticleDOI
TL;DR: Two new hybrids of FCM and improved self-adaptive PSO are presented, which combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions.
Abstract: We present two new hybrids of FCM and improved self-adaptive PSO.The methods are based on the FCM-PSO algorithm.We use FCM to initialize one particle to achieve better results in less iterations.The new methods are compared to FCM-PSO using many real and synthetic datasets.The proposed methods consistently outperform FCM-PSO in three evaluation metrics. Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM-IDPSO and FCM2-IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.

128 citations

Journal ArticleDOI
TL;DR: This paper discusses how fuzzy logic improves accuracy when forecasting time series using visibility graph and presents a novel method to make more accurate predictions and proves that the method has high flexibility and predictability.
Abstract: Time series attracts much attention for its remarkable forecasting potential. This paper discusses how fuzzy logic improves accuracy when forecasting time series using visibility graph and presents a novel method to make more accurate predictions. In the proposed method, historical data is firstly converted into a visibility graph. Then, the strategy of link prediction is utilized to preliminarily forecast the future data. Eventually, the future data is revised based on fuzzy logic. To demonstrate the performance, the proposed method is applied to forecast Construction Cost Index, Taiwan Stock Index and student enrollments. The results show that fuzzy logic is able to improve the accuracy by designing appropriate fuzzy rules. In addition, through comparison, it is proved that our method has high flexibility and predictability. It is expected that our work will not only make contributions to the theoretical study of time series forecasting, but also be beneficial to practical areas such as economy and engineering by providing more accurate predictions.

127 citations