Forecasting Rainfall in Surabaya Using the Singular Spectrum Analysis Method

Soehardjoepri*, Ulil Azmi, Ika Safitri, Ivan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Singular Spectrum Analysis (SSA) is a time series analysis method that uses a non-parametric approach. This study aims to determine the model and predict rainfall in Surabaya. The SSA process begins with decomposition consisting of embedding and singular value decomposition. Then a reconstruction process is carried out, which consists of eigentriple grouping and diagonal averaging. The determination of eigentriple grouping is based on changes in the value of the eigenvectors, which do not start much different between each eigenvector. This study will compare various window length (L) values. The best model obtained is with L = 72, MAD value is 151.9903, MSE is 32340.72, and sMAPE is 0.2679602. These results indicate that the best model will be obtained when using a maximum L, which is half of the total data or as much as 72. The results of rainfall forecasting using SSA cannot be used in the long term.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
EditorsRizhal Hendi Ristanto, Irwanto, Sri Rahayu, Tian Abdul Aziz, Dewi Muliyati
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735448018
Publication statusPublished - 12 Jan 2024
Event3rd Science and Mathematics International Conference, SMIC 2022 - Jakarta, Indonesia
Duration: 7 Nov 2022 → …

Publication series

NameAIP Conference Proceedings
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616


Conference3rd Science and Mathematics International Conference, SMIC 2022
Period7/11/22 → …


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