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Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Computer Modeling in Engineering & Sciences 2024年第4期139卷 187-224页

作者： Weisi Chen Walayat Hussain Francesco Cauteruccio Xu Zhang school of Software Engineering Xiamen University of TechnologyXiamen361024China Peter Faber business school Australian Catholic UniversityNorth Sydney2060Australia Department of Information Engineering Polytechnic University of MarcheAncona60121Italy

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last *** traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction ***,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and *** review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other *** illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related *** the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only *** remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency *** principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and t

关键词： Financial time series prediction convolutional neural network long short-term memory deep learning attention mechanism finance

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Systems science in the new era: intelligent systems and big data

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Science China(Information Sciences) 2024年第3期67卷 308-310页

作者： Wenwu YU Duxin CHEN Hongzhe LIU He WANG Jinde CAO Zengru DI Xiaojun DUAN Bin JIA Xiaodong DING Yiguang HONG school of Mathematics Southeast University Purple Mountain Laboratories International Academic Center of Complex Systems Beijing Normal University College of Science National University of Defense Technology school of Systems Science Beijing Jiaotong University business school University of Shanghai for Science and Technology Department of Control Science and Engineering Tongji University

This study introduces the emergence, research challenges,and future development of intelligent systems and big data(ISBD) and discusses the significance of ISBD to systems science. To this end, an overview of systems science is first presented, followed by the conclusion that the development of artificial intelligence(AI) has promoted the derivation and development of intelligent systems(ISs).

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Digital Revolution and Job Quality of Lower Class Workers: Quasi-experimental Evidence from the Broadband China Program

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China & World Economy 2024年第4期32卷 146-178页

作者： Xiumei Wang Ye Jiang Famin Yi school of business Fuyang Normal UniversityChina Department of Economics Dickinson CollegeUSA school of Economics and Management South China Agricultural UniversityChina

This study examines the impact of the digital revolution on the job quality of workers with a specific focus on those at the lowest end of the spectrum in China's domestic labor market. Leveraging exogenous timing variations that result from the implementation of the Broadband China Program across different cities, it provides quasi-experimental evidence concerning the effects of the digital revolution on the job quality of lower class workers, particularly rural–urban migrant workers. Using data from China Migrants Dynamic Survey, this study reveals that migrants' comprehensive job quality index increased by 0.0138 (5.33 percent), and its component indices experienced positive effects after the Broadband China Program was implemented. This study also investigates heterogeneous policy treatment effects among workers with diverse demographic and occupational characteristics. Underlying mechanisms are considered, including positive macroeconomic shocks, increased social interactions, and Internet use. In particular, migrants' attitudes toward local settlement became more positive, suggesting that the positive effects of the digital revolution on livelihood lasted for some time. Taking China as an example, this study contributes valuable empirical evidence of how the digital revolution can affect lower class workers' employment or living conditions in a developing country. Related policy implications are also considered.

关键词： Broadband China Program digital revolution job quality lower clas workers rural-urban migrants

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Central environmental protection inspection and carbon emission reduction: A tripartite evolutionary game model from the perspective of carbon neutrality

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Petroleum Science 2024年第3期21卷 2139-2153页

作者： Zhen-Hua Zhang Dan Ling Qin-Xin Yang Yan-Chao Feng Jing Xiu school of Economics Lanzhou UniversityLanzhou 730000GansuChina Institute of Green Finance Lanzhou UniversityLanzhou 730000GansuChina school of Management Lanzhou UniversityLanzhou 730000GansuChina school of business Zhengzhou UniversityZhengzhou 450001HenanChina Institute of Economics Jilin Academy of Social SciencesChangchun130033JilinChina

Since the carbon neutrality target was proposed,many countries have been facing severe challenges to carbon emission reduction *** study is conducted using a tripartite evolutionary game model to explore the impact of the central environmental protection inspection(CEPI)on driving carbon emission reduction,and to study what factors influence the strategic choices of each party and how they interact with each *** research results suggest that local governments and manufacturing enterprises would choose strategies that are beneficial to carbon reduction when CEPI *** the initial willingness of all parties increases 20%,50%—80%,the time spent for the whole system to achieve stability decreases from 100%,60%—30%.The evolutionary result of“thorough inspection,regulation implementation,low-carbon management”is the best strategy for the tripartite evolutionary ***,the smaller the cost and the larger the benefit,the greater the likelihood of the three-party game stability strategy *** study has important guiding significance for other developing countries to promote carbon emission reduction by environmental policy.

关键词： Central environmental protection inspection Local government Manufacturing enterprise Tripartite evolutionary game Carbon emission reduction

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A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets

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Computational Visual Media 2024年第1期10卷 161-186页

作者： Yu Dong Christy Jie Liang Yi Chen Jie Hua school of Computer Science University of Technology SydneySydneyNSW2007Australia Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing100048China

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological ***,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative *** address this issue,we developed a portrait-based visual modeling method called+*** method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological *** applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based *** perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case *** feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor *** adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the *** confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

关键词： visual modeling epidemiological analysis spatiotemporal multidimensional COVID-19

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Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

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Computers, Materials & Continua 2024年第3期78卷 3529-3549页

作者： Ying Su Morgan C.Wang Shuai Liu Department of Statistics and Data Science University of Central FloridaOrlandoFL32816-2370USA school of Mathematics and Statistics Beijing Technology and Business UniversityBeijing100048China

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual *** manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series *** complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple *** surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series ***,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series ***,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time *** is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing *** multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long ***,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural *** AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

关键词： Automated machine learning autoregressive integrated moving average neural networks time series analysis

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Ferric ion-triggered surface oxidation of galena for efficient chalcopyrite-galena separation

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International Journal of Minerals,Metallurgy and Materials 2024年第2期31卷 261-267页

作者： Qiancheng Zhang Limin Zhang Feng Jiang Honghu Tang Li Wang Wei Sun school of Minerals Processing and Bioengineering Central South UniversityChangsha 410083China Engineering Research Center of Ministry of Education for Carbon Emission Reduction in Metal Resource Exploitation and Utilization Changsha 410083China school of Resource and Environment Hunan University of Technology and BusinessChangsha 410083China

The efficient separation of chalcopyrite(CuFeS2)and galena(PbS)is essential for optimal resource ***,find-ing a selective depressant that is environmentally friendly and cost effective remains a *** various techniques,such as mi-croflotation tests,Fourier transform infrared spectroscopy,scanning electron microscopy(SEM)observation,X-ray photoelectron spec-troscopy(XPS),and Raman spectroscopy measurements,this study explored the use of ferric ions(Fe^(3+))as a selective depressant for *** results of flotation tests revealed the impressive selective inhibition capabilities of Fe^(3+)when used *** analysis showed that Fe^(3+)significantly reduced the adsorption of isopropyl ethyl thionocarbamate(IPETC)on the galena surface while having a minimal impact on *** analysis using SEM,XPS,and Raman spectra revealed that Fe^(3+)can oxidize lead sulfide to form compact lead sulfate nanoparticles on the galena surface,effectively depressing IPETC adsorption and increasing surface *** findings provide a promising solution for the efficient and environmentally responsible separation of chalcopyrite and galena.

关键词： galena chalcopyrite ferric ions flotation separation surface oxidation

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Approximation by Convolution Translation Networks on Conic Domains

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Acta Mathematica Sinica,English Series 2024年

作者： Bao Huai SHENG Dao Hong XIANG Department of Economic Statistics School of International BusinessZhejiang Yuexiu University school of of Mathematical Sciences Zhejiang Normal University Jinhua

We give investigations on the approximation order of translation networks produced by the convolution translation operators defined on a Jacobi cone and the surface cone. We deal with the convolution translation from the view of Fourier analysis, express the translation operator with orthogonal basis and provide a sufficient condition to ensure the density for the translation *** on these facts, we construct with the near best approximation operator and the Gauss integral formula two kinds of translation network operators and show their approximation orders in the best polynomial approximation.

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Radiation Spectral Analysis of 3D Dust Molecular Clusters(PAHs)and Peptoids under Ionization and Electric Field in ISM

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Research in Astronomy and Astrophysics 2024年第1期24卷 70-77页

作者： Ruiqing Wu Chunhua Zhu Guoliang Lü Xiaojiao Zhang Xizhen Lu Jinlong Yu Wujin Chen Mengqiu Long school of Physics and Electronics Central South UniversityChangsha 410083China school of Physical Science and Technology Xinjiang UniversityUrumqi 830046China school of Microelectronics and Physics Hunan University of Technology and BusinessChangsha 410205China College of Mechanical and Electronic Engineering Tarim UniversityAlar 843300China school of Medicine Xinjiang Medical UniversityUrumqi 830011China

Polycyclic aromatic hydrocarbons(PAHs),PANHs,and peptoids dust spectral calculations from the interstellar medium(ISM)are important for dust observations and *** goal is to calculate the radiation spectrum of spherical PAHs dust clusters in a vacuum containing ionized and applied in the presence of an electric *** propose a new simple computational model to calculate the size of three-dimensional spherical dust clusters formed by different initial dust *** the Vienna Ab-initio Simulation Package code,the density functional theory with the generalized approximation was used to calculate the electron density gradient and obtain the radiation spectrum of *** the radius of spherical dust clusters is~[0.009-0.042]μm,the dust radiation spectrum agrees well with the Z=0.02 mMMP stellar spectra,and the PAHs radiation spectrum of NGC 4676 at wavelengths of(0-5]μm and(5-10]μm,*** the ionized state,the N-PAH,C_(10)H_(9)N,2(C_(4)H_(4))^(1+),and peptoids 4(CHON),(C_(8)H_(10)N_(2)O_(5))^(1+)dust clusters at 3.3μm,while the 2(C_(22)H_(21)N_(3)O_(2))^(1+),4(CHON)dust clusters at 5.2μm have obvious *** is a characteristic of part of PAHs and peptoids clusters radiation at the nearinfrared wavelength of 2μ***,especially after applying an electric field to the dust,the emission spectrum of the dust increases significantly in the radiation wavelength range[3-10]μ***,the dust clusters of PAHs,PANHs,and peptoids of the radius size~[0.009-0.042]μm are likely to exist in the ISM.

关键词： ISM:structure ISM:molecules radiation mechanisms:general (ISM:)dust extinction

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ChatGPT, AI-generated content, and engineering management

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Frontiers of Engineering Management 2024年第1期11卷 159-166页

作者： Zuge YU Yeming GONG AIM(Artificial Intelligence and Management)Institute Emlyon Business SchoolEcully Cedex 69130France

This study explores the integration of ChatGPT and AI-generated content (AIGC) in engineering management. It assesses the impact of AIGC services on engineering management processes, mapping out the potential development of AIGC in various engineering functions. The study categorizes AIGC services within the domain of engineering management and conceptualizes an AIGC-aided engineering lifecycle. It also identifies key challenges and emerging trends associated with AIGC. The challenges highlighted are ethical considerations, reliability, and robustness in engineering management. The emerging trends are centered on AIGC-aided optimization design, AIGC-aided engineering consulting, and AIGC-aided green engineering initiatives.

关键词： engineering management AI-generated content(AIGC) ChatGPT AIGC-aided engineering lifecycle

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