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Window Length Selection of Singular Spectrum Analysis and Application to Precipitation Time SeriesView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000696</doi_batch_id><timestamp>1714106948</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 2</title></titles><publication_date><month>09</month><year>2024</year></publication_date><journal_volume><volume>19</volume></journal_volume><issue>2</issue></journal_issue><journal_article><titles><title>Window Length Selection of Singular Spectrum Analysis and Application to Precipitation Time Series</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>Window length is a very critical tuning parameter in Singular Spectrum Analysis (SSA) technique. For finding the optimal value of window length in SSA application, Periodogram analysis method with SSA for referencing on the selection of window length and confirm that the periodogram analysis can provide a good option for window length selection in the application of SSA. Several potential periods of Florida precipitation data are firstly obtained using periodogram analysis method. The SSA technique is applied to precipitation data with different window length as the period and experiential recommendation to extract the precipitation time series, which determines the leading components for reconstructing the precipitation and forecast respectively. A regressive model linear recurrent formula (LRF) model is used to discover physically evolution with the SSA modes of precipitation variability. Precipitation forecasts are deduced from SSA patterns and compared with observed precipitation. Comparison of forecasting results with observed precipitation indicates that the forecasts with window length of L=60 have the better performance among all. Our findings successfully confirm that the periodogram analysis can provide a good option for window length selection in the application of SSA and presents a detailed physical explanation on the varying conditions of precipitation variables.</p></abstract><publication_date><month>10</month><day>05</day><year>2017</year></publication_date><pages><first_page>306</first_page><last_page>317</last_page></pages><publisher_item><identifier id_type="pii">gnest_02117</identifier></publisher_item><doi_data><doi>10.30955/gnj.002117</doi><timestamp>1714106948</timestamp><resource>https://journal.gnest.org/publication/gnest_02117</resource></doi_data></journal_article></journal></body></doi_batch> </div>
