scholarly journals Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongxiang Sun ◽  
Zhongkai Yao ◽  
Qingchun Miao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Association Rules ◽  
Rapid Development ◽  
Financial Data ◽  
Prediction Algorithm ◽  
Economic Data ◽  
Growth Prediction ◽  
Macroeconomic Time Series ◽  
Macroeconomic Growth

With the rapid development of information technology and globalization of economy, financial data are being generated and collected at an unprecedented rate. Consequently, there has been a dire need of automated methods for effective and proficient utilization of a substantial amount of financial data to help in investment planning and decision-making. Data mining methods have been employed to discover hidden patterns and estimate future tendencies in financial markets. In this article, an improved macroeconomic growth prediction algorithm based on data mining and fuzzy correlation analysis is presented. This study analyzes the sequence of economic characteristics, reorganizes the spatial structure of economic characteristics, and integrates the statistical information of economic data. Using the optimized Apriori algorithm, the association rules between macroeconomic data are generated. Distinct features are extracted according to association rules using the joint distribution characteristic quantity of macroeconomic time series. Moreover, the Doppler parameter of macroeconomic time series growth prediction is calculated, and the residual analysis method of the regression model is used to predict the growth of macroeconomic data. Experimental results show that the proposed algorithm has better adaptability, less computation time, and higher prediction accuracy of economic data mining.

Data mining model for multimedia financial time series using information entropy

2020 ◽  
Vol 39 (4) ◽  
pp. 5339-5345
Author(s):  
Han He ◽  
Yuanyuan Hong ◽  
Weiwei Liu ◽  
Sung-A Kim
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Rapid Development ◽  
Development Trend ◽  
Series Data ◽  
Transaction Data ◽  
Relationship Analysis ◽  
Financial Time

At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

Study on Data Mining Techniques and Algorithms of Association Rules Data Mining

2014 ◽  
Vol 543-547 ◽  
pp. 2040-2044
Author(s):  
Yan Bo Wang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Granular Computing ◽  
Rapid Development ◽  
System Structure ◽  
Data Set ◽  
Mining Technology ◽  
Technology System ◽  
Operation Process ◽  
Database Technology

With the rapid development of network and database technology, data need to be processed massively increased, how to carry out effective data mining is a serious problem. The mature development of granular computing algorithm provides new ideas and new methods to study for data mining. Association rules of granular computing can reduce the number of object scanning data set, and improve the efficiency of the algorithm. In this paper we introduce the data source, classification, technology, system structure, operation process, application in other areas of data mining technology. Based on association rules of granular computing, data mining technology can provide quantitative basis for enterprise in screening assessment, so the service object has a stronger competitive advantage and focus more on its problems.

Research of Data Mining Algorithm Based on the Intrusion Prevention System

2014 ◽  
Vol 644-650 ◽  
pp. 1787-1790
Author(s):  
Xian Hong Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
User Behavior ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Current Model ◽  
Data Mining Algorithm ◽  
Intrusion Prevention ◽  
Mining Association Rules ◽  
Intrusion Prevention System

With the rapid development of network, the security problem of network becomes an issue which has been paid more and more attentions to. Among so many methods of intrusion prevention, data mining is a very effective one. The FP-growth algorithm is the most widely used algorithm for mining frequent item-sets, which is also an algorithm for mining association rules without candidate set. However, the FP-growth algorithm needs large memory when mining large database,and its running speed is slow. In order to overcome these problems, based on the FP-growth algorithm, this paper proposed an optimized algorithm. This paper compared the new algorithm with the previous one based on intrusion prevention model for campus network by experiments. Based on Experiments, we can draw the conclusion that, mining association rules by using the improved FP-growth algorithm can effectively detect the users’ behavior pattern, historical pattern and the current model to calculate the similarity of users, and provides the possibility to accurately judge the user behavior.

How Useful Can Be Data Mining For A Continuos Speech Therapist’s Education?

2014 ◽  
Vol 1 (1) ◽  
pp. 339-342
Author(s):  
Mirela Danubianu ◽  
Dragos Mircea Danubianu
Keyword(s):  
Data Mining ◽  
Information And Communication Technology ◽  
Association Rules ◽  
Communication Technology ◽  
Speech Therapy ◽  
Proper Treatment ◽  
Speech Impairments ◽  
Information And Communication ◽  
Specific Education

AbstractSpeech therapy can be viewed as a business in logopaedic area that aims to offer services for correcting language. A proper treatment of speech impairments ensures improved efficiency of therapy, so, in order to do that, a therapist must continuously learn how to adjust its therapy methods to patient's characteristics. Using Information and Communication Technology in this area allowed collecting a lot of data regarding various aspects of treatment. These data can be used for a data mining process in order to find useful and usable patterns and models which help therapists to improve its specific education. Clustering, classification or association rules can provide unexpected information which help to complete therapist's knowledge and to adapt the therapy to patient's needs.

Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

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