Efficient time series data classification using sliding window technique based improved association rule mining with  enhanced support vector machine

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.

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Association Rule Mining Using Time Series Data for Malaysia Climate Variability Prediction

Advances in Visual Informatics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70010-6_12 ◽

2017 ◽

pp. 120-130

Author(s):

Rabiatul A. A. Rashid ◽

Puteri N. E. Nohuddin ◽

Zuraini Zainol

Keyword(s):

Time Series ◽

Climate Variability ◽

Association Rule ◽

Association Rule Mining ◽

Time Series Data ◽

Series Data ◽

Rule Mining

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Prediction of water quality time series data based on least squares support vector machine

Procedia Engineering ◽

10.1016/j.proeng.2012.01.1162 ◽

2012 ◽

Vol 31 ◽

pp. 1194-1199 ◽

Cited By ~ 44

Author(s):

Guohua Tan ◽

Jianzhuo Yan ◽

Chen Gao ◽

Suhua Yang

Keyword(s):

Water Quality ◽

Time Series ◽

Support Vector Machine ◽

Least Squares ◽

Time Series Data ◽

Series Data ◽

Support Vector

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Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine

Journal of Discrete Mathematical Sciences and Cryptography ◽

10.1080/09720529.2020.1784535 ◽

2020 ◽

Vol 23 (8) ◽

pp. 1583-1597

Author(s):

Vijander Singh ◽

Ramesh Chandra Poonia ◽

Sandeep Kumar ◽

Pranav Dass ◽

Pankaj Agarwal ◽

...

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Time Series Data ◽

Series Data ◽

Support Vector ◽

Corona Virus

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Fuzzy Support Vector Machine for Pattern Classification of Time Series Data of KOSPI200 Index

Journal of Korean institute of intelligent systems ◽

10.5391/jkiis.2004.14.1.052 ◽

2004 ◽

Vol 14 (1) ◽

pp. 52-56 ◽

Cited By ~ 2

Author(s):

S.Y. Lee ◽

S.Y. Sohn ◽

C.E. Kim ◽

Y.B. Lee

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Pattern Classification ◽

Time Series Data ◽

Series Data ◽

Support Vector ◽

Fuzzy Support Vector Machine

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Effective Classification by Integrating Support Vector Machine and Association Rule Mining

Intelligent Data Engineering and Automated Learning – IDEAL 2006 - Lecture Notes in Computer Science ◽

10.1007/11875581_110 ◽

2006 ◽

pp. 920-927 ◽

Cited By ~ 5

Author(s):

Keivan Kianmehr ◽

Reda Alhajj

Keyword(s):

Support Vector Machine ◽

Association Rule ◽

Association Rule Mining ◽

Support Vector ◽

Rule Mining

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Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

International Journal of Computer Applications ◽

10.5120/17163-7223 ◽

2014 ◽

Vol 98 (3) ◽

pp. 23-28 ◽

Cited By ~ 1

Author(s):

Vinayak Choubey ◽

Satanand Mishra ◽

S. K. Pandey

Keyword(s):

Data Mining ◽

Time Series ◽

Support Vector Machine ◽

Real Time ◽

Surface Runoff ◽

Time Series Data ◽

Series Data ◽

Support Vector ◽

Time Series Data Mining ◽

Runoff Forecasting

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Finding opportunity windows in time series data using the sliding window technique: The case of stock exchanges

Econometrics ◽

10.15611/eada.2020.3.01 ◽

2020 ◽

Vol 24 (3) ◽

pp. 1-19

Author(s):

Necmi Gürsakal ◽

Fırat Melih Yilmaz ◽

Erginbay Uğurlu:

Keyword(s):

Time Series ◽

Time Series Data ◽

Stock Exchange ◽

Sliding Window ◽

Series Data ◽

Stock Exchanges ◽

Human Intelligence ◽

Window Technique

Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction

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Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine

Indonesian Journal of Applied Statistics ◽

10.13057/ijas.v4i1.47953 ◽

2021 ◽

Vol 4 (1) ◽

pp. 34

Author(s):

Bella Audina ◽

Mohamat Fatekurohman ◽

Abduh Riski

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Cash Flow ◽

Time Series Data ◽

Optimal Parameter ◽

Free Cash Flow ◽

Series Data ◽

Support Vector ◽

Operating Income ◽

Financial Condition

Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the Support Vector Machine (SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the Radial Basic Function kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the Free Cash Flow. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.Keywords: cash flow, forecasting, time series, support vector machine.

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