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Ling Tang
Beihang University
33Publications
13H-index
530Citations
Publications 33
Newest
Published on Mar 1, 2019in Applied Soft Computing 4.87
Fengmei Yang (Huada: Beijing University of Chemical Technology), Zhiwen Chen (Huada: Beijing University of Chemical Technology)+ 1 AuthorsLing Tang13
Estimated H-index: 13
(Beihang University)
Abstract The success of stock selection is contingent upon the future performance of stock markets. We incorporate stock prediction into stock selection to specifically capture the future features of stock markets, thereby forming a novel hybrid (two-step) stock selection method (involving stock prediction and stock scoring). (1) Stock returns for the next period are predicted using emerging computational intelligence (CI), i.e., extreme learning machine with a powerful learning capacity and a f...
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Published on Aug 1, 2018in Applied Soft Computing 4.87
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology),
Rongtian Zhou1
Estimated H-index: 1
(Huada: Beijing University of Chemical Technology)
+ 1 AuthorsRongda Chen2
Estimated H-index: 2
(Zhejiang University of Finance and Economics)
Abstract Credit risk assessment is often accompanied with sampling data imbalance. For this reason, this paper tries to propose a deep belief network (DBN) based resampling support vector machine (SVM) ensemble learning paradigm to solve imbalanced data problem in credit classification. In this paradigm, a bagging algorithm is first used to generate variable training subsets to make the subsets rebalanced and suitable in size. Then the SVM model is used as individual base classifier to formulate...
8 Citations Source Cite
Published on Aug 1, 2018in Energy 5.54
Ling Tang13
Estimated H-index: 13
(Beihang University),
Yao Wu2
Estimated H-index: 2
(Huada: Beijing University of Chemical Technology),
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology)
Abstract Inspired by the interesting idea of randomization, some powerful but time-consuming decomposition-ensemble learning paradigms can be extended into extremely efficient and fast variants by using randomized algorithms as individual forecasting tools. In the proposed methodology, Three major steps, (1) data decomposition via ensemble empirical mode decomposition, (2) individual prediction via a randomized algorithm (using randomization to mitigate training time and parameter sensitivity), ...
4 Citations Source Cite
Published on Apr 1, 2018in Journal of Forecasting 0.82
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology),
Zebin Yang1
Estimated H-index: 1
(HKU: University of Hong Kong),
Ling Tang13
Estimated H-index: 13
(Beihang University)
To guarantee stable quantile estimations even for noisy data, a novel loss function and novel quantile estimators are developed, by introducing the effective concept of orthogonal loss considering the noise in both response and explanatory variables. In particular, the pinball loss used in classical quantile estimators is improved into novel orthogonal pinball loss (OPL) by replacing vertical loss by orthogonal loss. Accordingly, linear quantile regression (QR) and support vector machine quantil...
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Published on Jan 1, 2018in International Journal of Forecasting 3.39
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology),
Yaqing Zhao1
Estimated H-index: 1
(Huada: Beijing University of Chemical Technology)
+ 1 AuthorsZebin Yang1
Estimated H-index: 1
(HKU: University of Hong Kong)
Abstract The rapid development of big data technologies and the Internet provides a rich mine of online big data (e.g., trend spotting) that can be helpful in predicting oil consumption — an essential but uncertain factor in the oil supply chain. An online big data-driven oil consumption forecasting model is proposed that uses Google trends, which finely reflect various related factors based on a myriad of search results. This model involves two main steps, relationship investigation and predict...
7 Citations Source Cite
Published on Jul 1, 2017in Applied Soft Computing 4.87
Ling Tang13
Estimated H-index: 13
(Beihang University),
Huiling Lv2
Estimated H-index: 2
(Huada: Beijing University of Chemical Technology),
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology)
An EEMD-based multi-scale fuzzy entropy approach is proposed to analyze the complexity characteristics of clean energy markets.The divide and conquer strategy is introduced to provide a more comprehensive complexity measurement tool for both the overall dynamics and various inner features with different time scales.The proposed EEMD-based multi-scale fuzzy entropy approach for complexity analysis can provide a new perspective for understanding market dynamics. To measure the efficiency of clean ...
13 Citations Source Cite
Published on Mar 1, 2017in Journal of Forecasting 0.82
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology),
Yang Zhao1
Estimated H-index: 1
(CAS: Chinese Academy of Sciences),
Ling Tang2
Estimated H-index: 2
(Huada: Beijing University of Chemical Technology)
Based on the concept of ‘decomposition and ensemble’, a novel ensemble forecasting approach is proposed for complex time series by coupling sparse representation (SR) and feedforward neural network (FNN), i.e. the SR-based FNN approach. Three main steps are involved: data decomposition via SR, individual forecasting via FNN and ensemble forecasting via a simple addition method. In particular, to capture various coexisting hidden factors, the effective decomposition tool of SR with its unique vir...
14 Citations Source Cite
Published on Feb 1, 2017in Applied Soft Computing 4.87
Ling Tang13
Estimated H-index: 13
(Huada: Beijing University of Chemical Technology),
Yao Wu2
Estimated H-index: 2
(Huada: Beijing University of Chemical Technology),
Lean Yu34
Estimated H-index: 34
(Huada: Beijing University of Chemical Technology)
Abstract To address time consuming and parameter sensitivity in the emerging decomposition- ensemble models, this paper develops a non-iterative learning paradigm without iterative training process. Different from the most existing decomposition-ensemble models using statistical or iterative approaches as individual forecasting tools, the proposed work otherwise employs the efficient and fast non-iterative algorithm—random vector functional link (RVFL) network with randomly fixed weights and dir...
19 Citations Source Cite
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