Comparison of ARIMA, SSA, and ARIMA – SSA hybrid model performance in Indonesian economic growth forecasting

Fajar, Muhammad and Hartini, Sri (2020): Comparison of ARIMA, SSA, and ARIMA – SSA hybrid model performance in Indonesian economic growth forecasting. Published in: The 2020 Asia-Pacific Statistics Week (16 June 2020)

Abstract

The aim of this research is to compare among the performance of ARIMA, Singular Spectrum Analysis (SSA), and ARIMA-SSA hybrid model which is applied to Indonesian economic growth forecasting. Data used in this research is economic growth (quarter to quarter, q to q) 1983 Q2 – 2018Q2 taken from Badan Pusat Statistik (BPS). The result of this research concludes that ARIMA-SSA hybrid method shows a better performance in economic growth forecasting compared to ARIMA and SSA based on the RMSE results.

• Item Type: MPRA Paper
• Language: English
• Keywords: hybrid model, ARIMA-SSA, forecasting, growth
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications

References

1. Aladag, C.H., Egrioglu, E. & Kadilar, C., 2012, Improvement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network American. Journal of Intelligent Systems 2(2), pp 12-17.

2. Alexandrov, Th., Golyandina, N., 2005, Automatic extraction and forecast of time series cyclic components within the framework of SSA. In Proceedings of the 5th St.Petersburg Workshop on Simulation, June 26 – July 2, 2005, St.Petersburg State University,St.Petersburg, pp 45–50.

3. Chai, T., & Draxler, R.R., 2014, Root mean square error or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature. Geosci. Model Dev 7, pp 1247- 1250.

4. Chang, P-C., Wang, Y-W., & Liu, C-H., 2007, The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications 32, pp 86–96.

5. Cryer, J.D. & Chan, K.S., 2008, Time series Analysis: With Application in R, Second Edition. USA: Spinger Science and Businiess Media, LLC.

6. Fajar, Muhammad, 2016, Perbandingan Kinerja Peramalan Pertumbuhan Ekonomi Indonesia antara ARMA, FFNN dan Hybrid ARMA-FFNN.DOI: 10.13140/RG.2.2.34924.36483.

7. Fajar, Muhammad, 2018., Meningkatkan Akurasi Peramalan dengan Menggunakan Metode Hybrid Singular Spectrum Analysis-Multilayer Perceptron Neural Networks. DOI:10.13140/RG.2.2.32839.60320.

8. Makridakis, W. & MacGee. 1999. Metode dan Aplikasi Peramalan. Jakarta: Binarupa Aksara.

9. Rahmani, Donya., 2014, A forecasting algorithm for Singular Spectrum Analysis based on bootstrap Linear Recurrent Formula coefficients. International Journal of Energy and Statistics 2 (4), pp 287–299.

10. Wei, W. W. S. 2006. Time series Analysis: Univariate and Multivariate Methods.California: Pearson Education, Inc.

11. Zhang, G.P. 2003. Time series Forecasting using a Hybrid ARIMA and Neural networks Model. Neurocomputing 50, pp 159-175.

12. Zhang, Q., Wang, B.D.,He,B., Peng, Y., & Ren, M.L. 2011. Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting. Water Resources Management 25, pp 2683-2703.