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.

Clustering of Time Series Data

2011 ◽  
pp. 258-263
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

scholarly journals Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA

2018 ◽  
Vol 2 (2) ◽  
pp. 49-57
Author(s):  
Dwi Yulianti ◽  
I Made Sumertajaya ◽  
Itasia Dina Sulvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Space Time ◽  
Series Data ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Sumatra Island ◽  
Rice Price ◽  
Multiple Variables

Generalized space time autoregressive integrated  moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

scholarly journals Construction of Complex Network with Multiple Time Series Relevance

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 202
Author(s):  
Zongwen Huang ◽  
Lingyu Xu ◽  
Lei Wang ◽  
Gaowei Zhang ◽  
Yaya Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Weather Conditions ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Optimal Network ◽  
Multiple Variables ◽  
Threshold Network

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

scholarly journals Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Munish Saini ◽  
Sandeep Mehmi ◽  
Kuljit Kaur Chahal
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Open Source ◽  
Open Source Software ◽  
Time Series Data ◽  
Series Data ◽  
Software Project ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Mining Algorithm

Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.

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