BibTex Citation Data :
@article{J.Gauss38594, author = {Diah Safitri and Gunardi Gunardi and Nanang Susyanto and winita Sulandari}, title = {PERAMALAN PADA RUNTUN WAKTU DENGAN POLA TREND MENGGUNAKAN SSA-LRF}, journal = {Jurnal Gaussian}, volume = {12}, number = {2}, year = {2023}, keywords = {SSA; SSA-LRF; Trend;Time Series}, 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 }, issn = {2339-2541}, pages = {296--303} doi = {10.14710/j.gauss.12.2.296-303}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/38594} }
Refworks Citation Data :
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
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