Hybrid Model of Singular Spectrum Analysis and ARIMA for Seasonal Time Series Data
| Autor: | Gumgum Darmawan, Dedi Rosadi, Budi N Ruchjana |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Cauchy: Jurnal Matematika Murni dan Aplikasi, Vol 7, Iss 2, Pp 302-315 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2086-0382 2477-3344 |
| DOI: | 10.18860/ca.v7i2.14136 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|