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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, L for 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 parameter L = T/5 was 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 for L = T/5 in 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 each L

},    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} }

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.
%X 

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, L for 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 parameter L = T/5 was 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 for L = T/5 in 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 each L

%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 %@ 2088-5334

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, L for 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 parameter L = T/5 was 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 for L = T/5 in 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 each L

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11653 DO - 10.18517/ijaseit.10.4.11653

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.
AB 

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, L for 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 parameter L = T/5 was 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 for L = T/5 in 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 each L

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11653 DO - 10.18517/ijaseit.10.4.11653