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Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data

Time series data can be reconstructed in terms of signal and noise components through Singular Spectrum Analysis method (SSA). In SSA, window length selection is important in ensuring that the signal and noise components are clearly separated. In general, the window length should be large enough but...

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Main Authors: Shaharudin, Shazlyn Milleana, Ahmad, Norhaiza, Yusof, Fadhilah
Format: Conference or Workshop Item
Published: 2015
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/59242/
http://dx.doi.org/10.1063/1.4907462
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spelling my.utm.592422021-08-09T07:29:36Z http://eprints.utm.my/id/eprint/59242/ Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data Shaharudin, Shazlyn Milleana Ahmad, Norhaiza Yusof, Fadhilah QA Mathematics Time series data can be reconstructed in terms of signal and noise components through Singular Spectrum Analysis method (SSA). In SSA, window length selection is important in ensuring that the signal and noise components are clearly separated. In general, the window length should be large enough but not greater than half of the observed time series data. However, different observed behaviour of a dataset might influence the selection of window length. In this study, we demonstrate the effect of window length on torrential rainfall time series data at three different scales in extracting the trend signals in rainfall data. The window lengths are compared using the classical SSA and another variation of SSA called iterative O-SSA. We use the minimum value of w-correlation to identify the window length that best measure clear separability between components. We found that a window length to the number of observed data of 6 shows a trend that fits well to the original rainfall time series data. 2015 Conference or Workshop Item PeerReviewed Shaharudin, Shazlyn Milleana and Ahmad, Norhaiza and Yusof, Fadhilah (2015) Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data. In: 2nd ISM International Statistical Conference 2014: Empowering the Applications of Statistical and Mathematical Sciences, ISM 2014, 12 August 2014 - 14 August 2014, Kuantan, Pahang, Malaysia. http://dx.doi.org/10.1063/1.4907462
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Shaharudin, Shazlyn Milleana
Ahmad, Norhaiza
Yusof, Fadhilah
Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
description Time series data can be reconstructed in terms of signal and noise components through Singular Spectrum Analysis method (SSA). In SSA, window length selection is important in ensuring that the signal and noise components are clearly separated. In general, the window length should be large enough but not greater than half of the observed time series data. However, different observed behaviour of a dataset might influence the selection of window length. In this study, we demonstrate the effect of window length on torrential rainfall time series data at three different scales in extracting the trend signals in rainfall data. The window lengths are compared using the classical SSA and another variation of SSA called iterative O-SSA. We use the minimum value of w-correlation to identify the window length that best measure clear separability between components. We found that a window length to the number of observed data of 6 shows a trend that fits well to the original rainfall time series data.
format Conference or Workshop Item
author Shaharudin, Shazlyn Milleana
Ahmad, Norhaiza
Yusof, Fadhilah
author_facet Shaharudin, Shazlyn Milleana
Ahmad, Norhaiza
Yusof, Fadhilah
author_sort Shaharudin, Shazlyn Milleana
title Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
title_short Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
title_full Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
title_fullStr Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
title_full_unstemmed Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
title_sort effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data
publishDate 2015
url http://eprints.utm.my/id/eprint/59242/
http://dx.doi.org/10.1063/1.4907462
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score 13.211869

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