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2020 3rd International Symposium on big data and Applied Statistics, ISBDAS 2020

作者： Zhan, Gaofeng Jiang, Yong Wang, Xiaofeng Zhao, Yuyang Zhao, Tong First Research Institute of the Ministry of Public Security Beijing100048 China Beijing Langrun Yihua Technology Co. Ltd Beijing100429 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100049 China

In the era of big data, the application of multi-source heterogeneous aggregation data is more and more extensive. If the quality of aggregation data is uneven, it will bring a lot of troubles to the subsequent data mining, and then lead to inaccurate decision-making. A comprehensive quality evaluation method for aggregation data is proposed in this paper, based on factor analysis and multivariate variance analysis which is from the perspective of multivariate statistical inference. The case study shows that the method proposed in this paper is feasible and adaptive for the long-term evaluation of the quality of multi-source heterogeneous aggregation data. © Published under licence by IOP Publishing Ltd.

关键词： Quality control

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A Study on The Multi-Attribute Decision Theory and Methods 9th

A Study on The Multi-Attribute Decision Theory and Methods

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Feng, Jinyuan Yan, Yunxi Huang, Manling Du, Yang Lu, Zhichen Li, Biao School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Dance Minzu University of China Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision theory

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Analysis of the Effectiveness of Carbon Emission Trading Market in China 10

Analysis of the Effectiveness of Carbon Emission Trading Mar...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Yu, Anqi Peng, Shuyang Li, Yizhi Guo, Kun School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Dornslife School of Arts and Science University of Southern California Los AngelesCA90007 United States Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As global climate change intensifies, carbon emissions trading has become an important tool for countries to address climate change. China, as the world's largest carbon emitter, has been conducting carbon trading pilots since 2011, and officially launched a national carbon market in 2017. The construction of China's carbon market is crucial for China and global efforts to address climate change. Market efficiency is a key indicator of its success. This article aims to measure and analyze the effectiveness of China's carbon market, and to explore policy measures to enhance its effectiveness. It selected data from effective trading days and conducted an exploratory study on the effectiveness of China's pilot carbon markets. To analyze the effectiveness of the carbon market, we combined the efficient market hypothesis theory with the fractal market hypothesis theory, adopted the variance ratio test method, and the rescaled range analysis method to conduct a comprehensive analysis of the Chinese carbon market, while also accounting for the reasons behind their computational inconsistency. As our research shows that China's pilot carbon market has not reached the weak efficiency level, we have provided some suggestions based on the current operational status of China's carbon market. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Carbon

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A hybrid model for stock price prediction based on multi-view heterogeneous data

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Financial Innovation 2024年第1期10卷 2411-2460页

作者： Wen Long Jing Gao Kehan Bai Zhichen Lu School of Economics and Management University of Chinese Academy of SciencesBeijing100190People’s Republic of China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing100190People’s Republic of China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100190People’s Republic of China Department of Mathematics Beijing Jiaotong UniversityBeijing100044People’s Republic of China

Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information ***,this study uses market data and news to investigate their joint impact on stock price ***,combining these two types of information is difficult because of their completely different *** study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information *** with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant *** the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s ***,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

关键词： Market data Financial news Support vector machine Multi-view learning Heterogeneous data

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Analysis on Marketing Ability and Financial Performance in Internet Company

Analysis on Marketing Ability and Financial Performance in I...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Zhuofan Yang Yong Shi Chinese Academy Sciences Research Center on Fictitious Economy & Data Science Beijing China Chinese Academy Sciences Key Research Laboratory of Big Data Mining and Knowledge Management Beijing China

This paper measures marketing ability and financial performance and examines what cause the overall operations inefficiency based on the operational process decomposition. The empirical research on e-portal and e-commerce company show that e-portal company has better marketing ability while e-commerce firm has better financial performance. For e-commerce company, it is marketing ability conversion inefficiency to lead to the overall operations inefficiency. In contrast, for e-portal company, lower financial performance conversion efficiency causes the overall operations inefficiency.

关键词： Companies Internet Social network services Portals Analytical models Couplings data models

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Exploring Freight Loading management by Deep Learning: a Case Study in Home Furnishing Industry

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Annals of data Science 2022年第2期9卷 213-228页

作者： Deng, Wei Patil, Rajvardhan Liu, Fangyao Daji, Ergu Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China College of Computer and Information Science Arkansas Tech University RussellvilleAR68182 United States College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Center on Fictitious Economy and Data Science The Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Nebraska Furniture Mart OmahaNE68124 United States Division of Science Research and Technology Southwest Minzu University Chengdu610041 China

Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management system is introduced. While most existing works on LSTM utilize the power of its prediction very well, our study shows less accurate and instable results if only LSTM network is applied, that arise from the big variation in the dataset. Instead, two constraints are inspired in this paper. The first constraint adds a time factor node to strengthen the correlation between predicted final loading units and booked loading units as close to delivery date. And the second proposal extends to parallel training by groupwise constraint. Experiments with across 3-year records in a national wide home furnishing retail company show that constraint LSTM networks significantly improve both the accuracy and stability of the prediction. Besides, the design of a smart loading management system will be shown, in which LSTM model plays the core rule to predict loading capacity, meanwhile the rule-based system triggers different events based on loading prediction and truck size. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

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Topic Selection Model Based on Network Group Intelligence 9th

Topic Selection Model Based on Network Group Intelligence

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Zhang, Lingling Xue, Bowen Huang, Siting School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Moe Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at Ucas Beijing100190 China Ping An Securities Group Limited Shenzheng518000 China

关键词： Collective Intelligence Consensus Analysis Social Network Topic Selection User influence

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Metric learning for multi-instance classification with collapsed bags

Metric learning for multi-instance classification with colla...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dewei Li Dongkuan Xu Jingjing Tang Yingjie Tian Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509061839

As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between bags in multi-instance problem, let alone learning appropriate metric for the problem. In this paper, we propose a new approach for multi-instance classification, with the idea of metric learning embedded. A new kind of distance is used to measure the similarity between bags. To weaken redundant information from bags and reduce computation complexity, k-means method is implemented to get collapsed bags by replacing each instance with its corresponding cluster centroid. The aim of metric learning is to expand inter-class bag distance and shrink intra-class bag distance, leading to the construction of an optimization problem with maximal relative distance. Kernel function can be introduced into the model to extract nonlinear information from the inputs. Gradient descent is utilized to solve the problem effectively. Numerical experiments on both artificial datasets and benchmark datasets demonstrated that the method can obtain competitive performance comparative to kNN and the state-of-the-art method in multi-instance classification.

关键词： Measurement Feature extraction data mining Optimization Kernel Training Drugs

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Prediction of Sequential Static Input-Output Table

Prediction of Sequential Static Input-Output Table

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Wen Long Huiwen Wang Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China School of Economics and Management Beihang University Beijing China

It is necessary to take much time and resources for compiling an input-output table, and there inevitably exists time lag in all the finished tables, which has greatly weakened the analysis and application function of input-output table. Though the development of techniques of adjusting input coefficient has relieved disadvantage of time lag to some extent, yet this kind of methods is still inapplicable to predictive problem. Consider that internal constraints of the table, this paper proposes a new approach based on the sequential tables series to solve the problem of forecasting the future input-output table.

关键词： Mathematical model Economics Forecasting Predictive models Matrix converters Industries

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Search Engine Marketing, Financing Ability and Firm Performance in E-commerce

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Procedia Computer Science 2015年 55卷 1106-1112页

作者： Zhuofan Yang Yong Shi Bo Wang Management of School University of Chinese Academy Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy Sciences Beijing 100190 China Key Research Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing 100190 China

This paper explores the relationship of search engine marketing, financing ability and e-commerce firm performance by the empirical research on China's B2C e-commerce firms. Results show that search engine marketing and business model has a strong positive relation with firm performance while financing ability has a negative effect on firm performance. It verifies the low returns to inputs in e-commerce and enlightens the managers should concentrate on business model innovation and consumer relation management.

关键词： Search Engine Marketing E-commerce Financing ability Firm performance

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