Hybrid Model of Singular Spectrum Analysis and ARIMA for Seasonal Time Series Data

Gumgum Darmawan, Dedi Rosadi, Budi N Ruchjana

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.


Keywords


ARIMA; automatic grouping; Long memory effect; Seasonal pattern; Singular Spectrum Analysis

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DOI: https://doi.org/10.18860/ca.v7i2.14136

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