Application of Singular Spectrum Analysis (SSA) Decomposition in Artificial Neural Network (ANN) Forecasting
DOI:
https://doi.org/10.21108/ijoict.v10i1.870Keywords:
Forecasting, Artificial Neural Network (ANN), Singular Spectrum Analysis (SSA), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Hybrid SSA – LSTM, Hybrid SSA – GRU, Hybrid SSA - ANNAbstract
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
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[1] S. Makridakis, S. C. Wheelwright, and V. E. McGee, Metode dan Aplikasi Peramalan. Jilid 1. Jakarta: Erlangga, 1999.
[2] K. E. ArunKumar, D. V. Kalaga, C. Mohan Sai Kumar, M. Kawaji, and T. M. Brenza, “Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends,” Alexandria Engineering Journal, vol. 61, no. 10, pp. 7585–7603, Oct. 2022, doi: 10.1016/j.aej.2022.01.011.
[3] I. Oxaichiko Arissinta, I. Dwi Sulistiyawati, and D. Kurnianto Iqbal Kharisudin, “Pemodelan Time Series untuk Peramalan Web Traffic Menggunakan Algoritma Arima,” Prosiding Seminar Nasional Matematika, vol. 5, pp. 693–700, 2022.
[4] Q. Tang, R. Shi, T. Fan, Y. Ma, and J. Huang, “Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/9942410.
[5] D. Alif Shafira and T. Wahyu Utami, "Peramalan Wisatawan Mancanegara Berkunjung Ke Bali Menggunakan Metode Singular Spectrum Analysis (SSA)". Prosiding Seminar Nasional Indonesian R Summit. vol.1, no 1. 2020.
[6] C. Li, Q. Guo, L. Shao, J. Li, and H. Wu, “Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network,” Electronics (Switzerland), vol. 11, no. 22, Nov. 2022, doi: 10.3390/electronics11223834.
[7] N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques. 2001.
[8] LazyProgrammer, Deep Learning: Recurrent Neural Networks in Python LSTM, GRU, and more RNN machine learning architectures in Python and Theano, 2016.
[9] R. Sarno, S. Izza Sabilla, M. S. Hanif Ardani, and D. Putra Purbawa, Machine Learning dan Deep Learning Konsep dan Pemrograman Python. Yogyakarta: Andi Publisher, 2022.
[10] J. Brownlee Disclaimer, Long Short-Term Memory Networks With Python Develop Sequence Prediction Models With Deep Learning. 2017.
[11] S. L. Zubaidi, J. Dooley, R. M. Alkhaddar, M. Abdellatif, H. Al-Bugharbee, and S. Ortega-Martorell, “A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks,” J Hydrol (Amst), vol. 561, pp. 136–145, Jun. 2018, doi: 10.1016/j.jhydrol.2018.03.047.
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