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Modified singular spectrum analysis in identifying rainfall trend over peninsular Malaysia

Shaharudin, S. M. and Ahmad, N. and Zainuddin, N. H. (2019) Modified singular spectrum analysis in identifying rainfall trend over peninsular Malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 15 (1). pp. 283-293. ISSN 2502-4752


Official URL: http://dx.doi.org/10.11591/ijeecs.v15.i1.pp283-293


Identifying the local time scale of the torrential rainfall pattern through Singular Spectrum Analysis (SSA) is useful to separate the trend and noise components. However, SSA poses two main issues which are torrential rainfall time series data have coinciding singular values and the leading components from eigenvector obtained from the decomposing time series matrix are usually assesed by graphical inference lacking in a specific statistical measure. In consequences to both issues, the extracted trend from SSA tended to flatten out and did not show any distinct pattern. This problem was approached in two ways. First, an Iterative Oblique SSA (Iterative O-SSA) was presented to make adjustment to the singular values data. Second, a measure was introduced to group the decomposed eigenvector based on Robust Sparse K-means (RSK-Means). As the results, the extracted trend using modification of SSA appeared to fit the original time series and looked more flexible compared to SSA.

Item Type:Article
Uncontrolled Keywords:singular spectrum analysis, spectrum analysis, trend
Subjects:Q Science > QA Mathematics
ID Code:90363
Deposited By: Narimah Nawil
Deposited On:18 Apr 2021 12:01
Last Modified:18 Apr 2021 12:01

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