# Cite Article

### Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data

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@article{IJASEIT11653, author = {Shazlyn Milleana Shaharudin and Norhaiza Ahmad and Nur Syarafina Mohamed and Nazrina Aziz}, title = {Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data}, journal = {International Journal on Advanced Science, Engineering and Information Technology}, volume = {10}, number = {4}, year = {2020}, pages = {1450--1456}, keywords = {singular spectrum analysis; trend; simulation; iterative o-ssa; robust sparse k-means; window length; modified singular spectrum analysis.}, abstract = {A popular method for time series analysis to extract the components of noise and trend from the time series data is called the singular spectrum analysis (SSA). However, the drawback of SSA is its problem in determining the appropriate window length,

}, issn = {2088-5334}, publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development}, url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11653}, doi = {10.18517/ijaseit.10.4.11653} }Lfor certain data set in confirming patent separation of the components of trend and noise. Another issue that crops up when using SSA is that, over time, the sum of day-to-day rainfall becomes nearly comparable. In this case, disjoints sets of singular values and distinctive series components could essentially be intermixed, resulting in poor separability between trend and noise components. The introduction of modified SSA is to mitigate the problems efficiently. The performance of modified SSA is measured by using w-correlation and RMSE based on simulated data. These results show that the parameterL = T/5was suitable to use in short time series rainfall data. It can be proved by the plot of the extracted trend for modified SSA that appears to conform to the original data configuration for time series rainfall however there is the omission of components of noise predominantly forL = T/5in detecting the uncharacteristically heavy downpour which could potentially initiate the occurrence of torrential rainfall. In addition, the result shows that average RMSE for reconstructed time series components of modified SSA is much smaller than SSA for eachL

## EndNote

%A Shaharudin, Shazlyn Milleana %A Ahmad, Norhaiza %A Mohamed, Nur Syarafina %A Aziz, Nazrina %D 2020 %T Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data %B 2020 %9 singular spectrum analysis; trend; simulation; iterative o-ssa; robust sparse k-means; window length; modified singular spectrum analysis. %! Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data %K singular spectrum analysis; trend; simulation; iterative o-ssa; robust sparse k-means; window length; modified singular spectrum analysis. %XA popular method for time series analysis to extract the components of noise and trend from the time series data is called the singular spectrum analysis (SSA). However, the drawback of SSA is its problem in determining the appropriate window length,

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11653 %R doi:10.18517/ijaseit.10.4.11653 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 4 %@ 2088-5334Lfor certain data set in confirming patent separation of the components of trend and noise. Another issue that crops up when using SSA is that, over time, the sum of day-to-day rainfall becomes nearly comparable. In this case, disjoints sets of singular values and distinctive series components could essentially be intermixed, resulting in poor separability between trend and noise components. The introduction of modified SSA is to mitigate the problems efficiently. The performance of modified SSA is measured by using w-correlation and RMSE based on simulated data. These results show that the parameterL = T/5was suitable to use in short time series rainfall data. It can be proved by the plot of the extracted trend for modified SSA that appears to conform to the original data configuration for time series rainfall however there is the omission of components of noise predominantly forL = T/5in detecting the uncharacteristically heavy downpour which could potentially initiate the occurrence of torrential rainfall. In addition, the result shows that average RMSE for reconstructed time series components of modified SSA is much smaller than SSA for eachL

## IEEE

Shazlyn Milleana Shaharudin,Norhaiza Ahmad,Nur Syarafina Mohamed and Nazrina Aziz,"Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data,"International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 4, pp. 1450-1456, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.4.11653.

## RefMan/ProCite (RIS)

TY - JOUR AU - Shaharudin, Shazlyn Milleana AU - Ahmad, Norhaiza AU - Mohamed, Nur Syarafina AU - Aziz, Nazrina PY - 2020 TI - Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 4 Y2 - 2020 SP - 1450 EP - 1456 SN - 2088-5334 PB - INSIGHT - Indonesian Society for Knowledge and Human Development KW - singular spectrum analysis; trend; simulation; iterative o-ssa; robust sparse k-means; window length; modified singular spectrum analysis. N2 -A popular method for time series analysis to extract the components of noise and trend from the time series data is called the singular spectrum analysis (SSA). However, the drawback of SSA is its problem in determining the appropriate window length,

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11653 DO - 10.18517/ijaseit.10.4.11653Lfor certain data set in confirming patent separation of the components of trend and noise. Another issue that crops up when using SSA is that, over time, the sum of day-to-day rainfall becomes nearly comparable. In this case, disjoints sets of singular values and distinctive series components could essentially be intermixed, resulting in poor separability between trend and noise components. The introduction of modified SSA is to mitigate the problems efficiently. The performance of modified SSA is measured by using w-correlation and RMSE based on simulated data. These results show that the parameterL = T/5was suitable to use in short time series rainfall data. It can be proved by the plot of the extracted trend for modified SSA that appears to conform to the original data configuration for time series rainfall however there is the omission of components of noise predominantly forL = T/5in detecting the uncharacteristically heavy downpour which could potentially initiate the occurrence of torrential rainfall. In addition, the result shows that average RMSE for reconstructed time series components of modified SSA is much smaller than SSA for eachL

## RefWorks

RT Journal Article ID 11653 A1 Shaharudin, Shazlyn Milleana A1 Ahmad, Norhaiza A1 Mohamed, Nur Syarafina A1 Aziz, Nazrina T1 Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data JF International Journal on Advanced Science, Engineering and Information Technology VO 10 IS 4 YR 2020 SP 1450 OP 1456 SN 2088-5334 PB INSIGHT - Indonesian Society for Knowledge and Human Development K1 singular spectrum analysis; trend; simulation; iterative o-ssa; robust sparse k-means; window length; modified singular spectrum analysis. ABLfor certain data set in confirming patent separation of the components of trend and noise. Another issue that crops up when using SSA is that, over time, the sum of day-to-day rainfall becomes nearly comparable. In this case, disjoints sets of singular values and distinctive series components could essentially be intermixed, resulting in poor separability between trend and noise components. The introduction of modified SSA is to mitigate the problems efficiently. The performance of modified SSA is measured by using w-correlation and RMSE based on simulated data. These results show that the parameterL = T/5was suitable to use in short time series rainfall data. It can be proved by the plot of the extracted trend for modified SSA that appears to conform to the original data configuration for time series rainfall however there is the omission of components of noise predominantly forL = T/5in detecting the uncharacteristically heavy downpour which could potentially initiate the occurrence of torrential rainfall. In addition, the result shows that average RMSE for reconstructed time series components of modified SSA is much smaller than SSA for eachL