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Review of graph construction and graph learning in stock price prediction

Procedia Computer Science 2022年 214卷 771-778页

作者： Yunong Wang Yi Qu Zhensong Chen Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Management and Engineering Capital University of Economics and Business Beijing 100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks.

关键词： Stock price prediction Graph construction Graph learning data structure

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The Further Development of Intelligent knowledge for Wisdom 9th

The Further Development of Intelligent Knowledge for Wisdom

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

作者： Gu, Jifa Xiao, Xiao Zhang, Lingling Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China 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

This paper introduces some modes from D, I, K to W, M. From different angles we investigate the various process. Especially we utilize the intelligent knowledge to develop the whole process. Finally we design a new medical hall for workshop of meta-synthetic engineering for helping patient to get the appropriate treatment and useful knowledge for health. © 2022 The Authors. Published by Elsevier B.V.

关键词： Patient treatment

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Semi-Supervised Concept Learning by Concept-Cognitive Learning and Concept Space

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2022年第5期34卷 2429-2442页

作者： Mi, YunLong Liu, Wenqi Shi, Yong Li, Jinhai Cent South Univ Sch Business Changsha 410083 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Kunming Univ Sci & Technol Data Sci Res Ctr Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming 650500 Yunnan Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

In human concept learning, people can naturally combine a handful of labeled data with abundant unlabeled data when they make classification decisions, which is also known as semi-supervised learning (SSL) in machine learning. Especially, human concept learning not only is a static process in human cognition but also can vary gradually with dynamic environments. Nevertheless, the classical SSL algorithms must be redesigned to accommodate newly input data. In this sense, concept-cognitive learning may be a good choice, as it can implement dynamic processes by imitating human cognitive processes. Meanwhile, numerous SSL methods were designed based on the feature vector information of instances, while ignoring concept structural information that is a very important process in human knowledge organization. Based on this idea, a novel SSL method, named semi-supervised concept learning method (S2CL), is proposed for dynamic SSL by employing concept spaces, in which knowledge is represented by hierarchical concept structures. Moreover, to make full use of the global and local conceptual information, we further propose an extended version of S2CL (namely, S2CL(alpha)) for concept learning. More specifically, to effectively exploit the unlabeled data, this paper first shows some new related theories for S2CL (or S2CL(alpha)) based on a regular formal decision context;then a novel SSL framework is designed, and its corresponding algorithm is developed. Finally, we conduct some experiments on various datasets to demonstrate the effectiveness of our methods, which include concept classification and incremental learning under a large quantity of unlabeled data.

关键词： Task analysis Heuristic algorithms Machine learning big data Semisupervised learning Clustering algorithms Concept-cognitive learning conceptual clustering top-K set similarity incremental learning semi-supervised learning

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The asset allocation utility of digital cryptocurrency

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Procedia Computer Science 2022年 214卷 1174-1181页

作者： Wen Long Maohua Wang Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance.

关键词： Digital Currency Hedging Effect Asset Allocation DCC-GARCH Model

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CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2023年第2期14卷 32-32页

作者： Yao, Jing Liu, Zheng Yang, Junhan Dou, Zhicheng Xie, Xing Wen, Ji-Rong Microsoft Res Asia Beijing 100080 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China

Deep semantic matching aims at discriminating the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document features and the extrinsic neighbor features to derive discrimination. Most of the existing works mainly care about how to utilize the presented neighbors, whereas limited effort is made to filter appropriate neighbors. We argue that the neighbor features could be highly noisy and partially useful. Thus, a lack of effective neighbor selection will not only incur a great deal of unnecessary computation cost but also restrict the matching accuracy severely. In this work, we propose a novel framework, Cascaded Deep Semantic Matching (CDSM), for accurate and efficient semantic matching on textual graphs. CDSM is highlighted for its two-stage workflow. In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost. We design both one-step and multi-step selection methods. In the second stage, a high-capacity graph-based matching network is employed to compute fine-grained relevance scores based on the well-selected neighbors. It is worth noting that CDSM is a generic framework which accommodates most of the mainstream graph-based semantic matching networks. The major challenge is how the selector can learn to discriminate the neighbors' usefulness which has no explicit labels. To cope with this problem, we design a weak-supervision strategy for optimization, where we train the graph-based matching network at first and then the ad-hoc neighbor selector is learned on top of the annotations from the matching network. We conduct extensive experiments with three large-scale datasets, showing that CDSM notably improves the semantic matching accuracy and efficiency thanks to the selection of high-quality neighbors. The source code is rele

关键词： Semantic matching textual graph neighbor selection

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Assessing the extent and persistence of major crisis events in the crude oil market and economy: evidence from the past 30 years

HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS

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HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2024年第1期11卷 903页

作者： Zhang, Qi Hu, Yi Jiao, Jianbin Wang, Shouyang Beijing Technol & Business Univ Business Sch Beijing 100048 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Emergency Management Sci & Engn Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China ShanghaiTech Univ Sch Entrepreneurship & Management Shanghai 201210 Peoples R China

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience.

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Latest Technologies on dataset Distillation: A Survey

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Procedia Computer Science 2024年 242卷 1112-1117页

作者： Muyang Li Yi Qu Yong Shi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets.

关键词： dataset Distillation Survey Meta-learning framework data matching framework

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Quaternion Ordinal Embedding 31

Quaternion Ordinal Embedding

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31st International Joint Conference on Artificial Intelligence (IJCAI)

作者： Hou, Wenzheng Xu, Qianqian Ma, Ke Hao, Qianxiu Huang, Qingming Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Peng Cheng Lab Artificial Intelligence Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9781956792003

Ordinal embedding (OE) aims to project objects into a low-dimensional space while preserving their ordinal constraints as well as possible. Generally speaking, a reasonable OE algorithm should simultaneously capture a) semantic meaning and b) the ordinal relationship of the objects. However, most of the existing methods merely focus on b). To address this issue, our goal in this paper is to seek a generic OE method to embrace the two features simultaneously. We argue that different dimensions of vector-based embedding are naturally entangled with each other. To realize a), we expect to decompose the D dimensional embedding space into D different semantic subspaces, where each subspace is associated with a matrix representation. Unfortunately, introducing a matrix-based representation requires far more complex parametric space than its vector-based counterparts. Thanks to the algebraic property of quaternions, we are able to find a more efficient way to represent a matrix with quaternions. For b), inspired by the classic chordal Grassmannian distance, a new distance function is defined to measure the distance between different quaternions/matrices, on top of which we construct a generic OE loss function. Experimental results for different tasks on both simulated and real-world datasets verify the effectiveness of our proposed method.

关键词： Embeddings

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MaxMatch: Semi-Supervised Learning With Worst-Case Consistency

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第5期45卷 5970-5987页

作者： Jiang, Yangbangyan Li, Xiaodan Chen, Yuefeng He, Yuan Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Alibaba Grp Secur Dept Hangzhou 311121 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management BDKM Beijing 101408 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this article. Specifically, we first present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants. We then provide a simple but effective algorithm to solve the proposed minimax problem, and theoretically prove that it converges to a stationary point. Experiments on five popular benchmark datasets validate the effectiveness of our proposed method.

关键词： Predictive models Training data models Semantics Perturbation methods Computational modeling Benchmark testing Semi-supervised learning consistency regularization worst-case consistency image classification

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Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying

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Procedia Computer Science 2024年 242卷 662-668页

作者： Hailiang Jiang Xin Tian Ye Tao Lulu Wang Research Center on Fictitious Economy and Data Science. Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Beijing HAOLINJU CVS Co. Ltd Beijing 100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai 201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers; fresh produce stores outperform regular stores in fruit sales of CGB; Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels.

关键词： community group-buying subjectivity fresh produce stores dark-colored fruits

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