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CPS1889 Subanar
Further investigations will be conducted and presented in the extended
version of this paper.
References
1. Adhikari, R., & Agrawal, R. K. (2012). Forecasting strong seasonal time
series with artificial neural networks. Journal of Scientific and Industrial
Research, 71(October 2012), 657–666.
2. Di Persio, L., & Honchar, O. (2016). Artificial neural networks approach to
the forecast of stock market price movements. International Journal of
Economics and Management Systems, 1.
3. Elsner, J. B. (2002). Analysis of Time Series Structure: SSA and Related
Techniques. Journal of the American Statistical Association, 97(460),
1207–1208. https://doi.org/10.1198/jasa.2002.s239
4. Golyandina, N. (2010). On the choice of parameters in singular spectrum
analysis and related subspace-based methods. Stat Interface, 3(3), 259–
279.
5. Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. (2001). Analysis of Time
Series Structure: SSA and related techniques. Chapman & Hall/CRC, Boca
Raton, FL.
6. Golyandina, N., & Zhigljavsky, A. (2013). Singular Spectrum Analysis for
time series. Springer Science & Business Media.
7. Hassani, H. (2007). Singular Spectrum Analysis: Methodology and
Comparison. Journal of Data Science, 5, 239–257.
8. Huang, L., & Wang, J. (2018). Forecasting energy fluctuation model by
wavelet decomposition and stochastic recurrent wavelet neural network.
Neurocomputing.
9. Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q)
model for timeseries forecasting. Expert Systems with Applications,
37(1), 479–489.
10. Sulandari, W., Subanar, S., Lee, M. H., & Rodrigues, P. C. (2019).
Indonesian electricity load forecasting using singular spectrum analysis,
fuzzy systems, and neural networks. Manuscript submitted for
publication.
11. Wu, C. L., & Chau, K. W. (2011). Rainfall–runoff modeling using artificial
neural network coupled with singular spectrum analysis. Journal of
Hydrology, 399(3-4), 394–409.
12. Wu, C. L., Chau, K. W., & Li, Y. S. (2009). Methods to improve neural
network performance in daily flows prediction. Journal of Hydrology,
372(1-4), 80–93.
13. Yolcu, U., Egrioglu, E., & Aladag, C. H. (2013). A new linear & nonlinear
artificial neural network model for time series forecasting. Decision
Support Systems, 54(3), 1340–1347.
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