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All Journal IJoICT (International Journal on Information and Communication Technology)
Irwan Girana
Mathematics Department, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung, Indonesia
Computer Science & IT

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Application of Singular Spectrum Analysis (SSA) Decomposition in Artificial Neural Network (ANN) Forecasting Annisa Martina; Irwan Girana
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.870

Abstract

Over time, various forecasting methods have been introduced. An example is the Hybrid model. This model can enhance the forecast accuracy compared to a single model. The Hybrid Singular Spectrum Analysis (SSA)-Artificial Neural Network (ANN) model combines the concepts of decomposition and forecasting. The Hybrid SSA-ANN forecasting works through two stages. Firstly, SSA decomposes the data into trend, seasonal, noise, and residue components. Secondly, the decomposed data is predicted using the ANN model, specifically the LSTM and GRU models. The Hybrid SSA-ANN model has been proven to improve forecasting accuracy. The Hybrid SSA-LSTM model improves the forecast accuracy by 78% compared to the single LSTM forecasting model. This can be seen from the respective RMSE values of 4.36 changing to 0.97 and MAPE values of 5.2% changing to 1.16%. Similarly, the Hybrid SSA-GRU model improves the forecast accuracy by 79% compared to the single GRU forecasting model. This can be observed from the respective RMSE values of 4.86 changing to 1.01 and MAPE values of 6.33% changing to 1.36%. In a case study using weekly data of crude oil's opening prices, the application of SSA decomposition can enhance the forecast accuracy by 78-79% in ANN forecasting
Co-Authors Annisa Martina