Financial Big Data Analysis and Early Warning Platform: A Case Study

Liang, Yi; Quan, Daiyong; Wang, Fang; Jia, Xiaoli; Li, Menggang; Li, Ting

doi:10.1109/access.2020.2969039

Cited by 19 publications

(9 citation statements)

References 18 publications

(12 reference statements)

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“…Finally, in order to have a better understanding of the big amount of economic data analyzed from various cross-disciplines (e.g., cloud computing, artificial intelligence and machine learning, macroeconomic forecasting, and microeconomic analysis) Liang et al in [ 62 ] researched economic and security big data collection while focusing on multi-source heterogeneous data fusion algorithms and cleaning techniques in order to create a style suitable for data analysis of economic security. The authors also proceeded with the construction of different big data computing frameworks, real-time risk early-warning analysis algorithms for the real-time and delay needs of big data early-warning analysis of economic security, and deeply explored the relationship between different industries and regional economies.…”

Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Each computing server node allocates and divides some partitions of all training data sets. After all the calculation tasks are completed, the weight results obtained from the calculation task will be aggregated into the parameter server, and the average evaluation and other operations will be carried out to update them to the shared parameters [20].…”

Section: Parallel Gradient Computing Parallel Microbatch Sgd Algorith...mentioning

confidence: 99%

Big Data Analysis of Marketing User Intelligence Information Based on Deep Learning

Shen

2022

Mobile Information Systems

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To solve the problem that the explosive growth of data cannot be effectively analyzed, a big data analysis and prediction system based on deep learning is proposed. The systems proposed in this paper optimize the design of circulatory neural networks using an optimization processing strategy in addition to circulatory neural networks based on a distributed parallel processing framework that studies and analyzes disrupted neural networks during the in-depth study. The results are as follows: using the DCNNPS parallel strategy can effectively improve the training speed of the convolutional neural network to a small extent, and the speed can be improved by more than 50%. Compared with the other two, the network optimized by DCNNPS is less sensitive to data growth in the scenario of a large amount of data and is more suitable for processing a large amount of data. Moreover, with the increase of computing nodes, the acceleration effect is more obvious, the acceleration ratio is higher, and the improvement energy is about 70%. It is proved that the system proposed in this paper can process massive data very effectively and make a great contribution to the development of the industry.

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“…Many financial institutions are currently exploring innovative ways of using big data analysis to improve their internal risk assessment systems [77]. The development of better models identifying highrisk areas could improve the tools available to regulators for early detection of potential financial crises.…”

Section: Economic and Financial Risks Forecastingmentioning

confidence: 99%

Risk Forecasting in the Light of Big Data

Kernchen¹

2021

JRACR

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Life in modern society is increasingly connected by networks that link the world around us and create numerous exciting opportunities, new services and advantages for humanity. Yet concurrently, these underpinning networks have provided routes by which potentially dangerous and harmful incidents can propagate quickly and worldwide. This complexity poses a considerable challenge for risk analysis and forecasting. Conventional methods of risk analysis tend to underestimate the probability and impact of risks (e.g. pandemics, financial collapses, terrorist attacks), as sometimes the existence of independent observations is wrongly assumed and cascading errors that can occur in complex systems are not considered. The purpose of this article is to assess critically the potential of big data to profoundly change the current capability for risk forecasting in diverse areas and the assertion that big data leads to better risk predictions. In particular, the focus is on big data implications for risk forecasting in the areas of economic and financial risks, environmental and sustainable development risks, and public and national security risks. The article concludes that big data and predictive analytics offer substantial opportunities for improving risk forecasting but may not replace the significance of appropriate assumptions, adequate data quality and continuous validation.

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Financial Big Data Analysis and Early Warning Platform: A Case Study

Cited by 19 publications

References 18 publications

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Big Data Analysis of Marketing User Intelligence Information Based on Deep Learning

Risk Forecasting in the Light of Big Data

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