scholarly journals Optimization of Quantitative Financial Data Analysis System Based on Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meiyi Liang
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Performance Test ◽  
Learning Algorithm ◽  
User Interaction ◽  
Financial Data ◽  
Similar Time ◽  
Financial Data Analysis ◽  
Analysis System ◽  
Data Analysis System

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results.

scholarly journals Development of International Agricultural Trade Using Data Mining Algorithms-Based Trade Equality

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liu Yan
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Agricultural Trade ◽  
Machine Learning Techniques ◽  
Trade Data ◽  
Data Mining Algorithms ◽  
Efficient Data ◽  
Using Data ◽  
Analysis System ◽  
Data Analysis System

The development of international agriculture trade during the COVID-19 pandemic has encountered significant challenges. The processing of international agricultural trade data using machine learning techniques needs to be improved to perform effective analysis of agricultural trade. An essential issue for international agricultural trade is the accurate yield estimation for the numerous crops involved in international trade. Data mining techniques are the necessary approach for accomplishing practical and effective solutions for this problem. This paper combined the bidirectional encoder representations from transformers (BERT) model to conduct data mining and developed a trade data analysis system with efficient data analysis capabilities. Our results indicate that our model does reasonably well and obtains adequate information in deciding international agricultural trade. It can also be instrumental for policy and decision-making regarding international agricultural trade.

scholarly journals Discussion on the Application of Enterprise Business Intelligence Data Analysis System

2021 ◽  
Vol 5 (4) ◽  
pp. 23-26
Author(s):  
Ning Yang
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Business Intelligence ◽  
Customer Requirements ◽  
Analysis System ◽  
Data Analysis System ◽  
Comprehensive Processing

Enterprise Business Intelligence (BI) system refers to data mining through the existing database of the enterprise, and data analysis according to customer requirements through comprehensive processing. The data analysis efficiency is high and the operation is convenient. This paper mainly analyzes the application of enterprise BI data analysis system in enterprises.

scholarly journals Big data sets in construction

2019 ◽  
Vol 110 ◽  
pp. 02007
Author(s):  
Pavel Kagan
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Text Analysis ◽  
Text Messages ◽  
Data Conversion ◽  
Data Sets ◽  
Analysis System ◽  
The Moment ◽  
Data Analysis System

The paper studies the processing of large information data arrays (Big Data) in construction. The issues of the applicability of the big data concept (Big Data) at various stages of the life cycle of buildings and structures are considered. Methods for data conversion for their further processing are proposed. The methods used in the analysis of "big data" allow working with unstructured data sets (Data Mining). An approach is considered, in which the analysis of arbitrary data can be reduced to text analysis, similar to the analysis of ordinary text messages. At the moment, it is important and interesting to isolate non-obvious links present in the analysed data. The advantage of using big data is that it is not necessary to advance hypotheses for testing. Hypotheses appear during data analysis. Dependence analysis is a basic approach when working with big data. The concept of an automatic big data analysis system is proposed. For data mining, text analysis algorithms should be used, and discriminant functions should be used for the main problem to be solved (data classification).

scholarly journals A review of data mining methods in financial markets

2021 ◽  
Vol 1 (4) ◽  
pp. 362-392
Author(s):  
Haihua Liu ◽  
◽  
Shan Huang ◽  
Peng Wang ◽  
Zejun Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Financial Markets ◽  
Social Life ◽  
Development Trend ◽  
Financial Data ◽  
Support Vector ◽  
Mining Methods ◽  
Financial Data Analysis ◽  
Using Data

<abstract><p>Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.</p></abstract>

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