Hybrid Model of Singular Spectrum Analysis and ARIMA for Seasonal Time Series Data - Darmawan, Gumgum; Rosadi, Dedi; Ruchjana, Budi N (CAUCHY, 2022)
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.