Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Gaussian
Nanang Susyanto
Departemen Matematika, Universitas Gadjah Mada
Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PERAMALAN PADA RUNTUN WAKTU DENGAN POLA TREND MENGGUNAKAN SSA-LRF Diah Safitri; Gunardi Gunardi; Nanang Susyanto; winita Sulandari
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.296-303

Abstract

Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF) is a forecasting method that starts by decomposing time series data into several independent and interpretable components. SSA-LRF does not have any assumptions that must be fulfilled thus it is more flexible to use. In this research, an empirical study of time series forecasting that has a trend data pattern will be carried out using SSA-LRF without difference transformation and with difference transformation. A difference transformation is performed because the data has a trend pattern. Although there are no assumptions that must be met in forecasting using SSA-LRF, it is expected that difference transformation will produce better forecasting accuracy than without difference transformation process. There are three data used in this research. The first is data from Wei's book (2006), this data is called series W8 and is a simulation data. The second data is the number of railway passengers in the Java region. The third data is Mauna Loa atmospheric CO2 concentration data obtained from R software. Forecasting using SSA-LRF without difference transformation and with difference transformation on all three data resulted in accurate forecasting values, and difference transformation improved the accuracy values
Co-Authors Diah Safitri Gunardi . winita Sulandari