ACADSTAFF UGM
| Title | : | Hybrid Model of Singular Spectrum Analysis and ARIMA for Seasonal Time Series Data |
| Author | : |
GUMGUM DARMAWAN (1) Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2) Budi Nurani Ruchjana (3) |
| Date | : | 1 2022 |
| Keyword | : | ARIMA; automatic grouping; Long memory effect; Seasonal pattern; Singular Spectrum Analysis ARIMA; automatic grouping; Long memory effect; Seasonal pattern; Singular Spectrum Analysis |
| Abstract | : | Hybrid models between Singular Spectrum Analysis (SSA) and Autoregressive Integrated Moving Average (ARIMA) have been developed by several researchers. In the SSA-ARIMA hybrid model, SSA is used in the decomposition and reconstruction process, while forecasting is done through the ARIMA model. In this paper, hybrid SSA-ARIMA uses two auto grouping models. The first model, namely the Alexandrov method and the second method, is alternative auto grouping with a long memory approach. The two-hybrid models were tested for two types of seasonal patterns, multiplicative and additive seasonal time series data. The analysis results using both methods give accurate results; as seen from the MAPE generated the 12 observations for the future, the value is below 5%. The hybrid SSA-ARIMA method with Alexandrov auto grouping is more accurate for an additive seasonal pattern, but the hybrid SSA-ARIMA with alternative auto grouping is more accurate for a multiplicative seasonal pattern. |
| Group of Knowledge | : | Statistik |
| Original Language | : | English |
| Level | : | Nasional |
| Status | : |
Published
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| No | Title | Action |
|---|---|---|
| 1 |
Gum gum Cauchy 14136-43167-1-PB.pdf
Document Type : [PAK] Full Dokumen
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