摘要：
ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 mu m in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10(5) charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.
摘要：
Fabrication of ZnO/graphene hybrid by homogeneous depositing a continuous and conformable ZnO layer on graphene remains a great challenge and highly desired. In this paper, well-defined flake-like ZnO/graphene hybrid was fabricated by a facile in situ hydrolysis-deposition route using zinc acetate and graphene oxide (GO) as starting materials and ammonia as precipitator and reductant. By rational control the hydrolysis and condensation of Zn precursor, ZnO nanocrystals are homogeneously anchored on graphene due to the strong interaction between ZnO and GO, which was simultaneously reduced to graphene. The photocatalytic activity of the photocatalyst was evaluated using Brilliant Red X-3B (X3B) dye as probe. It was found that the presence of GO enhanced the crystallization of ZnO, and the photocatalytic activity of ZnO/graphene hybrid increases first and then decreases with increasing the amount GO. The hybrid with 3 wt.% of GO shows the highest photocatalytic activity, which is 3 times higher than that of pure ZnO. (C) 2015 Elsevier B.V. All rights reserved.
摘要：
Virus-host association studies are significant for understanding the complex functions and dynamics of microbial communities of human health or diseases. Several virus-host association prediction methods have been developed based on the information of sequences, virus networks, host networks and virus-host networks separately. In this study, we develop a heterogeneous network approach based on neighborhood regularization logistic matrix factorization (LMFH-VH) which integrate the virus similarity network and the host similarity network using known virus-host associations. The virus similarity network and the host similarity network were constructed based on oligonucleotide frequency measures and Gaussian interaction profile kernel similarity, respectively. LMFH-VH achieves the best performance on several validation datasets comparing with other four network-based methods. The host prediction accuracy of LMFH-VH is 24.17% and 12.8% higher than two recently proposed virus-host prediction methods, respectively. The codes and datasets are available at https://github.com/liudan111/LMFH-VH.git.
作者机构：
[Hu, Xiaohua; Li, Xusheng; He, Tingting; Wang, Xiaoyan; Zhong, Duo; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Sch Comp & Informat, Philadelphia, PA 19104 USA.;[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Li, Xusheng;Wang, Xiaoyan;Zhong, Duo;He, Tingting;Hu, Xiaohua;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Sch Comp & Informat, Philadelphia, PA 19104 USA.^[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
biomedical text mining;bacterial named entity recognition;conditional random field;deep learning;microbial interaction
摘要：
Microorganisms have been confirmed to be essential for the fundamental function of various ecosystems. The interactions among microorganisms affect the human health and environmental ecosystem. A large number of microbial interactions with experimental confidence have been reported in biomedical literature. Extracting and collating these interactions with experimental confidence into a database will create a valuable data resource. Named Entity Recognition (NER) is the premise and key to interaction extraction from literatures. Especially, bacterial named entity recognition is still a challenging task due to the specialty of bacterial names. In this paper, we propose a bacterial named entity recognition system based on a hybrid deep learning framework (HDL-CRF), which integrates two deep learning models: the bidirectional long short-term memory network and the convolutional neural network, as well as the conditional random field approach, for automatically extracting the features. Finally, we prove that this model outperforms previous methods in performance.
作者机构：
[Shen, Xianjun; Hu, Xiaohua; Gong, Xue; He, Tingting; Jiang, Xingpeng; Yang, Jincai] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Shen, Xianjun;Gong, Xue;Jiang, Xingpeng;Yang, Jincai;He, Tingting;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
weighted Directed motifs;microbial network;high order structures;motif-based clustering
摘要：
High-order connectivity patterns are essential to understanding the basic structure of complex networks. Network motifs are considered as the basic building blocks of complex networks. From identifying network motifs to discovering higher-order modular organizations by them, it is helpful to study the organization principles and functional modules of the biological networks in a divide-and-conquer manner. However, the current research based on network motifs often neglect the influence of weight in network motifs. In this paper, the concept of weighted motifs was presented and was applied to microbial network. The method was proposed to find the optimal weighted motif in microbial network and analyze the high-order structure of weighted networks based on them. It also proved that the partially weighted motifs can obtain optimal clusters in theory over unweighted ones.
作者机构：
[Hu, Xiaohua; Pan, Min; He, Tingting; Zhu, Qing; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.;[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.^[Zhu, Qing;Pan, Min;Jiang, Xingpeng;Hu, Xiaohua;He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
Microorganisms are closely related to human health and have an impact on the development of various diseases. It is extremely significant to identify the relationships between microorganisms and the phenotypes (such as healthy or disease status) by analyzing microbial abundance in personalized medicine. Deep learning allows computational models that composed of multiple processing layers to learn representation of data with multiple levels of abstraction. These methods have improved the state-of-the-art in speech recognition, visual object recognition and object detection. However, current deep models are typically neural networks which are actually multiple layers of parameterized differentiable nonlinear models that can be trained by backpropagation. It is interesting to explore other deep learning models to handle tasks with small sample size and high dimensional data. While a unique feature of microbial data is that it has phylogenetic tree structure information which can be embedded to improve the classification performance. In this work, in order to further improve the metagenomic classification, we propose a deep model named Cascade Deep Forest which keeps the spatial structure between nodes through embedding phylogenetic tree information. Our results demonstrate: 1) the modified cascade structure can enhance the classification performance of Deep Forest; 2) embedding phylogenetic tree information can also improve the classification of the models; 3) Deep Forest achieves highly competitive performance to deep neural networks.
作者机构：
[Hu, Xiaohua; Pan, Min; He, Tingting; Jiang, Xingpeng; Liu, Lei; Li, Bojing; Hu, XH] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.;[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.^[Pan, Min;Liu, Lei;Li, Bojing;He, Tingting;Jiang, Xingpeng;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
With the rapid advancement of DNA sequencing, metagenomics and metatranscriptomics have made great progress, which deepen our understanding on the human microbiome and its impact on human health and diseases. The microbiome, which is characterized by small samples, high dimensions and complicated relationships with hosts, refers to the species, genes and genomes of the microbiota, as well as the products of the microbiota and the host environment. In fact, many machine learning methods have been used to conduct Microbiome-Wide Association Studies which can link the microbiome with the phenotypes, such as the status of human health and diseases. However, existing methods such as Support Vector Machines (SVMs) have some limitations on deep representation learning with deep architectures which can promote the reuse of features and potentially lead to progressively more abstract features at higher layers of representations. Recently, Deep Neural Networks (DNNs), a kind of deep learning models, are widely used for metagenomic data analysis and can perform well on representation learning. But they are considered as a black box and sufferring from criticisms due to theirs lacking of interpretability. Thus, it is interesting to explore other deep learning models for metagenomic data analysis. In this work, we introduce a deep learning model called Deep Forest to study the microbiome associations and we also present an ensemble method for feature selection. Experimental results show that Deep Forest outperforms the traditional machine learning methods. In addition, compared to DNNs, Deep Forest has better interpretability and less hyperparameters.
作者机构：
[Liu, Yan; Lin, Xuan; Li, Kenli; Quan, Zhe] Hunan Univ, Coll Informat Sci & Engn, Changsha, Hunan, Peoples R China.;[Wang, Zhi-Jie] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China.;[Wang, Zhi-Jie] Guangdong Key Lab Big Data Anal & Proc, Guangzhou, Guangdong, Peoples R China.;[Wang, Zhi-Jie] Natl Engn Lab Big Data Anal & Applicat, Beijing, Peoples R China.;[Wang, Fan] Cent China Normal Univ, Coll Chem, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Quan, Zhe;Lin, Xuan;Liu, Yan;Li, Kenli] Hunan Univ, Coll Informat Sci & Engn, Changsha, Hunan, Peoples R China.^[Wang, Zhi-Jie] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China.^[Wang, Zhi-Jie] Guangdong Key Lab Big Data Anal & Proc, Guangzhou, Guangdong, Peoples R China.^[Wang, Zhi-Jie] Natl Engn Lab Big Data Anal & Applicat, Beijing, Peoples R China.^[Wang, Fan] Cent China Normal Univ, Coll Chem, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
machine learning;drug discovery;neural networks;molecule data
摘要：
In recent years, researchers in the fields of bioinformatics and cheminformatics have attempted to utilize machine learning methods for molecule modeling, bioactivity prediction, chemical property prediction, biology analysis, etc. In this paper, we present a system that merges the merits of various techniques such as long short-term memory (LSTM) recurrent neural networks, and is designed for learning atoms and solving the classic problems such as single task classification in the field of drug discovery. We have implemented our approach and conducted extensive experiments based on several widely used datasets such as SIDER and Tox21. The experimental results consistently demonstrate the feasibility and superiority of our proposed approach.
作者机构：
[Luo, Xiaofeng] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Luo, Xiaofeng] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.
通讯机构：
[Luo, Xiaofeng] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.
会议名称：
25th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions
会议时间：
SEP 27-OCT 03, 2015
会议地点：
Sci Council Japan, Kobe, JAPAN
会议主办单位：
[Luo, Xiaofeng] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.^[Luo, Xiaofeng] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.
摘要：
Beam energy scan programs in heavy-ion collisions aim to explore the QCD phase structure at high baryon density. Sensitive observables are applied to probe the signatures of the QCD phase transition and critical point in heavy-ion collisions at RHIC and SPS. Intriguing structures, such as dip, peak and oscillation, have been observed in the energy dependence of various observables. In this paper, an overview is given and corresponding physics implications will be discussed for the experimental highlights from the beam energy scan programs at the STAR, PHENIX and NA61/SHINE experiments. Furthermore, the beam energy scan phase II at RHIC (2019–2020) and other future experimental facilities for studying the physics at low energies will be also discussed.
摘要：
Two triazole-modified calix[4]arene diesters were synthesized via Huisgen 1,3-dipolar cycloaddition between azides esters and alkynylcalixarenes. Their structures had been deduced from H-1 NMR, element analysis and ESI-MS. Two-phase extraction experiments indicated that triazole-modified calix[4]arene diethylester 3a exhibited Cs+ selectivity.
期刊：
Journal of Agricultural and Food Chemistry,2012年60(18):4480-4491 ISSN：0021-8561
通讯作者：
Gu, Yu-Cheng
作者机构：
[Yang, Guang-Fu; Wu, Qiong-You; Huang, Shao-Wei; Lin, Long] Cent China Normal Univ, Coll Chem, Minist Educ, Key Lab Pesticide & Chem Biol, Wuhan 430079, Peoples R China.;[Irwin, Dianne; Clough, John; Gu, Yu-Cheng; Mulholland, Nick; Beattie, David] Syngenta Jealotts Hill Int Res Ctr, Bracknell RG42 6EY, Berks, England.
通讯机构：
[Gu, Yu-Cheng] Syngenta Jealotts Hill Int Res Ctr, Bracknell RG42 6EY, Berks, England.
会议主办单位：
(1) Key Laboratory of Pesticide and Chemical Biology, College of Chemistry, Central China Normal University, Wuhan 430079, China; (2) Syngenta Jealotts Hill International Research Centre, Bracknell, Berkshire RG42 6EY, United Kingdom
作者机构：
[Shi, Shusu] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Nucl Sci, Berkeley, CA 94720 USA.;[Shi, Shusu] Huazhong Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.;[Shi, Shusu] Huazhong Normal Univ, Minist Educ, Key Lab Quark & Lepton Phys, Wuhan 430079, Hubei, Peoples R China.;[Shi, Shusu] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Nucl Sci, 1 Cyclotron Rd, Berkeley, CA 94720 USA.
通讯机构：
[Shi, Shusu] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Nucl Sci, 1 Cyclotron Rd, Berkeley, CA 94720 USA.
会议名称：
21st International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter 2009)
会议时间：
MAR 30-APR 04, 2009
会议地点：
Knoxville, TN
会议主办单位：
[Shi, Shusu] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Nucl Sci, Berkeley, CA 94720 USA.^[Shi, Shusu] Huazhong Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.^[Shi, Shusu] Huazhong Normal Univ, Minist Educ, Key Lab Quark & Lepton Phys, Wuhan 430079, Hubei, Peoples R China.
摘要：
Collective flow reflects the dynamical evolution in high-energy heavy ion collisions. In particular, the elliptic flow reflects early collision dynamics [1]. We present a systematic analysis of elliptic flow (v(2)) for identified particles measured in Au + Au and Cu + Cu collisions at root s(NN) = 200 GeV. Number of quark scaling is tested in the intermediate p(T) region and in the smaller system (Cu + Cu). The Cu + Cu collisions results are compared with those from ideal hydrodynamic model calculations.
作者机构：
[Shao, Yu] Univ Sci & Technol China, Coll Mat Sci & Engn, Anhua 230026, Peoples R China.;[Liu, Rujun; Yang, Jianhua; Huang, Sheng; Yu, Ying; Huang, Yuan] Cent China Normal Univ, Coll Phys Sci & Technol, Inst Nanosci & Nanotechnol, Wuhan 430079, Peoples R China.
通讯机构：
[Yu, Ying] Cent China Normal Univ, Coll Phys Sci & Technol, Inst Nanosci & Nanotechnol, Wuhan 430079, Peoples R China.
会议名称：
4th International Symposium on Environmental Science and Technology (ISEST)
会议时间：
JUN 04-07, 2013
会议地点：
Dalian, PEOPLES R CHINA
会议主办单位：
[Yang, Jianhua;Liu, Rujun;Huang, Sheng;Huang, Yuan;Yu, Ying] Cent China Normal Univ, Coll Phys Sci & Technol, Inst Nanosci & Nanotechnol, Wuhan 430079, Peoples R China.^[Shao, Yu] Univ Sci & Technol China, Coll Mat Sci & Engn, Anhua 230026, Peoples R China.
关键词：
Ga-doped CdS;Semiconductor photocatalysts;Theoretical calculation;Activity and stability improvement
摘要：
Herein, a series of Ga-doped CdS photocatalysts are successfully prepared by simple solvothermal method using absolute ethyl alcohol as only reaction solvent. The samples have been characterized by X-ray diffraction, scanning electron microscope, UV-vis diffuse reflectance spectrum, X-ray photoelectron spectroscopy and electrochemical measurements. Their photocatalytic activity has been evaluated by the photocatalytic degradation of rhodamine B (RhB) under simulated visible-light irradiation. It is found that the prepared samples are interstitial Ga-doped CdS, which has never been reported before. The photocatalytic activity of Ga-doped CdS with molar ratio of Ga:Cd = 1:50 for RhB degradation can be enhanced by 114% in comparison with that of pure CdS under same reaction conditions. All of the data based on theoretical calculation and experiment indicate that Ga ion doped into the interstice of crystal lattice in CdS can not only lower the valence band position but also decrease the formation energy of the CdS samples. What is more, Ga ion doping can widen the valence band of CdS semiconductor, which is quite beneficial for the separation of photo-generated carriers. As a result, the Ga-doped CdS not only has improved photocatalytic activity for the degradation of pollutant pollutants but also has enhanced stability under visible-light irradiation. In a word, Ga-doped CdS will be a promising photocatalyst with richly potential application prospect under solar light. (C) 2013 Elsevier B.V. All rights reserved.
会议名称：
5th International Symposium on Advances in Visual Computing, ISVC 2009
会议时间：
November 30, 2009 - December 2, 2009
会议地点：
Las Vegas, NV, United states
会议主办单位：
(1) School of Informatics, University of Edinburgh, United Kingdom; (2) Engineering and Research Centre for Information Technology on Education, Huazhong Normal University, Wuhan, China
作者机构：
[Wei, Tianxiang; Dai, Zhihui] Nanjing Normal Univ, Sch Chem & Mat Sci, Jiangsu Collaborat Innovat Ctr Biomed Funct Mat, Nanjing 210023, Jiangsu, Peoples R China.;[Wei, Tianxiang; Dai, Zhihui] Nanjing Normal Univ, Sch Chem & Mat Sci, Jiangsu Key Lab Biofunct Mat, Nanjing 210023, Jiangsu, Peoples R China.;[Wei, Tianxiang; Du, Dan; Lin, Yuehe] Washington State Univ, Sch Mech & Mat Engn, Pullman, WA 99164 USA.;[Du, Dan; Lin, Yuehe] Cent China Normal Univ, Coll Chem, Key Lab Pesticides & Chem Biol, Minist Educ PR China, Wuhan 430079, Peoples R China.
通讯机构：
[Du, Dan] Washington State Univ, Sch Mech & Mat Engn, Pullman, WA 99164 USA.;Cent China Normal Univ, Coll Chem, Key Lab Pesticides & Chem Biol, Minist Educ PR China, Wuhan 430079, Peoples R China.
会议名称：
2015 E-MRS Fall Meeting (European-Materials-Research-Society) Symposium R entitled Nanocarbon Electrochemistry and Interfaces
会议时间：
SEP 15-18, 2015
会议地点：
Warsaw, POLAND
会议主办单位：
[Wei, Tianxiang;Dai, Zhihui] Nanjing Normal Univ, Sch Chem & Mat Sci, Jiangsu Collaborat Innovat Ctr Biomed Funct Mat, Nanjing 210023, Jiangsu, Peoples R China.^[Wei, Tianxiang;Dai, Zhihui] Nanjing Normal Univ, Sch Chem & Mat Sci, Jiangsu Key Lab Biofunct Mat, Nanjing 210023, Jiangsu, Peoples R China.^[Wei, Tianxiang;Lin, Yuehe;Du, Dan] Washington State Univ, Sch Mech & Mat Engn, Pullman, WA 99164 USA.^[Lin, Yuehe;Du, Dan] Cent China Normal Univ, Coll Chem, Key Lab Pesticides & Chem Biol, Minist Educ PR China, Wuhan 430079, Peoples R China.
摘要：
Immunoassay is a common analytic method with great practicability. Recently, there is a growing interest in applying graphene to modern biosensor research due to its distinct physical and chemical properties, such as excellent conductivity, high surface area, ease of functionalization, and so on. In this review, we summarized the recent researches on the development of electrochemical immunoassays based on graphene. We focus on the different roles of graphene in constructing various electrochemical immunosensors and provide a perspective about this research field.
期刊：
Journal of Systems Science and Complexity,2014年27(1):225-236 ISSN：1009-6124
通讯作者：
Liu John
作者机构：
[Xiao Yi] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Xiao Jin] Sichuan Univ, Sch Business, Chengdu 610064, Peoples R China.;[Liu John] City Univ Hong Kong, Ctr Transport Trade & Financial Studies, Hong Kong, Hong Kong, Peoples R China.;[Wang Shouyang] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China.
通讯机构：
[Liu John] City Univ Hong Kong, Ctr Transport Trade & Financial Studies, Hong Kong, Hong Kong, Peoples R China.
会议名称：
1st International Conference on Forecasting Economic and Financial Systems (FEFS) / 5th International Workshop on Singular Spectrum Analysis and its Applications (SSA)
会议时间：
MAY 17-20, 2012
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Xiao Yi] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.^[Xiao Jin] Sichuan Univ, Sch Business, Chengdu 610064, Peoples R China.^[Liu John] City Univ Hong Kong, Ctr Transport Trade & Financial Studies, Hong Kong, Hong Kong, Peoples R China.^[Wang Shouyang] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China.
摘要：
The financial market volatility forecasting is regarded as a challenging task because of irregularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components (i.e., approximation and details) using the maximum overlap discrete wavelet transform (MODWT). The approximation is predicted by a hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) with feedforward neural network (FNN). ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.
