Inducing Fuzzy Association Rules with Multiple Minimum Supports for Time Series Data

2014 ◽  
pp. 437-445
Author(s):  
Rakesh Rathi ◽  
Vinesh Jain ◽  
Anshuman Kumar Gautam
Keyword(s):  
Time Series ◽  
Association Rules ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Association Rules ◽  
Multiple Minimum Supports

Study on Mining Association Rules from Stock Time Series Data

2013 ◽  
Vol 303-306 ◽  
pp. 1119-1124
Author(s):  
Xian Tan
Keyword(s):  
Time Series ◽  
Set Theory ◽  
Association Rules ◽  
Rough Set ◽  
Data Reduction ◽  
Stock Price ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Series Data ◽  
Extraction Study

Rough set theory is a kind of ambiguity and imprecision new mathematical tools, using precise mathematical analysis of imprecise system an ideal method. Rough set theory has powerful data reduction capability, this paper rough set theory to model the stock time series data, reduction, rule extraction, study the ups and downs of the relationship between the stock price, the use of advanced data mining techniques to dig out price linkage between stock association rules, has a very important significance.

Study on Data Classification and Trend of Association Rules of Time Series

2014 ◽  
Vol 644-650 ◽  
pp. 2164-2168
Author(s):  
Yong Zhi Liu ◽  
Xue Ping Jia
Keyword(s):  
Time Series ◽  
Association Analysis ◽  
Association Rules ◽  
Significant Role ◽  
Continuous Time ◽  
Time Series Data ◽  
Data Classification ◽  
Series Data ◽  
Analysis Of Time Series

Association rules has played a significant role in mining classification clear affairs, but the performance is poor for the continuous time series data . Firstly, this paper presents the trend of time series, including the rise, decline and steady trend, and the time series trend method is proposed; Secondly, define the trend of association rules, including the trend of association rules’ support degree, trend of association rule’s confidence; Finally, gives an application example, show the effectiveness of the method in classification and association analysis of time series.

scholarly journals TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 365
Author(s):  
Qiang Zhao ◽  
Qing Li ◽  
Deshui Yu ◽  
Yinghua Han
Keyword(s):  
Time Series ◽  
Association Rules ◽  
Pattern Mining ◽  
Time Series Data ◽  
Series Data ◽  
Association Rules Mining ◽  
The Public ◽  
Mining Algorithm ◽  
Temporal Relationships ◽  
Multiple Variables

In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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