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

Cauchy: Jurnal Matematika Murni dan Aplikasi

Gumgum Darmawan¹  Budi N Ruchjana²  Dedi Rosadi² 

[1] Gadjah Mada University, Yogyakarta, Indonesia;Padjadjaran University, Bandung, Indonesia;

关键词: arima;    automatic grouping;    long memory effect;    seasonal pattern;    singular spectrum analysis;   

DOI  :  10.18860/ca.v7i2.14136

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown