How to cite this paper
Setiawan, S., Fajar, M., Yasin, H & Lande, C. (2023). Hybrid SSA-TBATS to improve forecasting accuracy on export value data in Indonesia.International Journal of Data and Network Science, 7(4), 1505-1514.
Refrences
Aydln, S., Saraoǧlu, H. M., & Kara, S. (2011). Singular spectrum analysis of sleep EEG in insomnia. Journal of Medical Systems, 35(4), 457–461. https://doi.org/10.1007/s10916-009-9381-7.
Balassa, B. (1978). EXPORTS AND ECONOMIC GROWTH Farther evidence. In Journal of Development Jiconomics (Vol. 5). Holland Publishing Company.
Bank Indonesia. (2022, November 17). BI 7-Day Reverse Repo Rate Naik 50 bps Menjadi 5,25%: Sinergi Menjaga Stabili-tas dan Momentum Pemulihan . Siaran Pers. https://www.bi.go.id/id/publikasi/ruang-media/news-release/Pages/sp_2431322.aspx.
de Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771.
Fajar, M. (2019). An Application of Hybrid Forecasting Singular Spectrum Analysis – Extreme Learning Machine Method in Foreign Tourists Forecasting. Jurnal Matematika “MANTIK,” 5(2), 60–68. https://doi.org/10.15642/mantik.2019.5.2.60-68.
Ghodsi, M., & Yarmohammadi, M. (2014). Exchange rate forecasting with optimum singular spectrum analysis. Journal of Systems Science and Complexity, 27(1), 47–55. https://doi.org/10.1007/s11424-014-3303-6
Ginting, A. M. (2017). ANALISIS PENGARUH EKSPOR TERHADAP PERTUMBUHAN EKONOMI INDONESIA. Buletin Ilmiah Litbang Perdagangan, 11(1), 1–20. https://doi.org/https://doi.org/10.30908/bilp.v11i1.185.
Golyandina, N. E., Alexandrov, T., & Golyandina, N. (2005). Automatic extraction and forecast of time series cyclic com-ponents within the framework of SSA Singular Spectrum Analysis View project Automatic extraction and forecast of time series cyclic components within the framework of SSA. https://www.researchgate.net/publication/237743635.
Golyandina, N., & Korobeynikov, A. (2014). Basic Singular Spectrum Analysis and forecasting with R. Computational Statistics and Data Analysis, 71, 934–954. https://doi.org/10.1016/j.csda.2013.04.009
Golyandina, N., & Zhigljavsky, A. (2013). Singular Spectrum Analysis for Time Series. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34913-3.
Hassani, H., Heravi, S., & Zhigljavsky, A. (2009). Forecasting European industrial production with singular spectrum analysis. International Journal of Forecasting, 25(1), 103–118. https://doi.org/10.1016/j.ijforecast.2008.09.007.
Hassani, H., Soofi, A. S., & Zhigljavsky, A. A. (2010). Predicting daily exchange rate with singular spectrum analysis. Nonlinear Analysis: Real World Applications, 11(3), 2023–2034. https://doi.org/10.1016/j.nonrwa.2009.05.008.
Hassani, H., & Zhigljavsky, A. (2009). SINGULAR SPECTRUM ANALYSIS: METHODOLOGY AND APPLICATION TO ECONOMICS DATA *. Jrl Syst Sci & Complexity, 22, 372–394.
Helpman, E., & Krugman, P. (1985). Market Structure and Foreign Trade: Increasing Returns, Imperfect Competition, and the International Economy. The MIT Press.
Jung, W. S., & Marshall, P. J. (1985). GROWTH AND CAUSALITY IN DEVELOPING COUNTRIES. Journal of Devel-opment Economics, 18(1), 1–12.
Kavoussi, R. M. (1984). Export expansion and economic growth: further empirical evidence. Journal of Development Economics, 14, 241–250.
Khan, M. A. R., & Poskitt, D. S. (2012). Moment tests for window length selection in singular spectrum analysis of short- and long-memory processes. Journal of Time Series Analysis, 34(2), 141–155. https://doi.org/10.1111/j.1467-9892.2012.00820.x.
Khan, M. A. R., & Poskitt, D. S. (2013). A Note on window length selection in singular spectrum analysis. Australian and New Zealand Journal of Statistics, 55(2), 87–108. https://doi.org/10.1111/anzs.12027.
Krueger, A. O. (1980). American Economic Association Trade Policy as an Input to Development. Source: The American Economic Review, 70(2), 288–292.
Kumar, U., & Jain, V. K. (2010). Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy, 35(4), 1709–1716. https://doi.org/10.1016/j.energy.2009.12.021.
Marques, C. A. F., Ferreira, J. A., Rocha, A., Castanheira, J. M., Melo-Gonçalves, P., Vaz, N., & Dias, J. M. (2006). Singu-lar spectrum analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth, 31(18), 1172–1179. https://doi.org/10.1016/j.pce.2006.02.061.
Menezes, R., Dionísio, A., & Hassani, H. (2012). On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries. Quarterly Review of Eco-nomics and Finance, 52(4), 369–384. https://doi.org/10.1016/j.qref.2012.10.002
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Source: Biometrika, 75(2), 335–381.
Qu, Z. (2011). A test against spurious long memory. Journal of Business and Economic Statistics, 29(3), 423–438. https://doi.org/10.1198/jbes.2010.09153.
Srinivasan, T. N., & Bhagwati, J. N. (1979). Trade Policy and Development. In International Economic Policy: Theory and Evidence (1st ed., pp. 1–38). Johns Hopkins University Press. https://www.researchgate.net/publication/248145710.
Suhartono, Isnawati, S., Salehah, N. A., Prastyo, D. D., Kuswanto, H., & Lee, M. H. (2018). Hybrid SSA-TSR-ARIMA for water demand forecasting. International Journal of Advances in Intelligent Informatics, 4(3), 238–250. https://doi.org/10.26555/ijain.v4i3.275.
Sulandari, W., Subanar, Suhartono, Utami, H., Lee, M. H., & Rodrigues, P. C. (2020). Ssa-based hybrid forecasting mod-els and applications. Bulletin of Electrical Engineering and Informatics, 9(5), 2178–2188. https://doi.org/10.11591/eei.v9i5.1950.
Sumiyarti, S. (2015). APAKAH HIPOTESIS “EXPORT LED GROWTH” BERLAKU DI INDONESIA? Jurnal Ekonomi & Studi Pembangunan (JESP), 16(2), 188–199.
Thomakos, D. D., Wang, T., & Wille, T. (2002). Modeling daily realized futures volatility with singular spectrum analy-sis. In Physica A (Vol. 312). www.elsevier.com/locate/physa.
Tsay, R. S. (1986). Nonlinearity tests for time series. Biometrika, 73(2), 461–467.
Unnikrishnan, P., & Jothiprakash, V. (2020). Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall. Water Re-sources Management, 34(11), 3609–3623. https://doi.org/10.1007/s11269-020-02638-w
Wang, J., & Li, X. (2018). A combined neural network model for commodity price forecasting with SSA. Soft Computing, 22(16), 5323–5333. https://doi.org/10.1007/s00500-018-3023-2.
Wen, F., Xiao, J., He, Z., & Gong, X. (2014). Stock price prediction based on SSA and SVM. Procedia Computer Science, 31, 625–631. https://doi.org/10.1016/j.procs.2014.05.309.
Yang, B., Dong, Y., Yu, C., & Hou, Z. (2016). Singular Spectrum Analysis Window Length Selection in Processing Capac-itive Captured Biopotential Signals. IEEE Sensors Journal, 16(19), 7183–7193. https://doi.org/10.1109/JSEN.2016.2594189.
Zhang, H., Yang, Y., Zhang, Y., He, Z., Yuan, W., Yang, Y., Qiu, W., & Li, L. (2021). A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting. Neural Computing and Applications, 33(2), 773–788. https://doi.org/10.1007/s00521-020-05113-0.
Zhang, Q., Wang, B. de, He, B., Peng, Y., & Ren, M. L. (2011). Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting. Water Resources Management, 25(11), 2683–2703. https://doi.org/10.1007/s11269-011-9833-y.
Balassa, B. (1978). EXPORTS AND ECONOMIC GROWTH Farther evidence. In Journal of Development Jiconomics (Vol. 5). Holland Publishing Company.
Bank Indonesia. (2022, November 17). BI 7-Day Reverse Repo Rate Naik 50 bps Menjadi 5,25%: Sinergi Menjaga Stabili-tas dan Momentum Pemulihan . Siaran Pers. https://www.bi.go.id/id/publikasi/ruang-media/news-release/Pages/sp_2431322.aspx.
de Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771.
Fajar, M. (2019). An Application of Hybrid Forecasting Singular Spectrum Analysis – Extreme Learning Machine Method in Foreign Tourists Forecasting. Jurnal Matematika “MANTIK,” 5(2), 60–68. https://doi.org/10.15642/mantik.2019.5.2.60-68.
Ghodsi, M., & Yarmohammadi, M. (2014). Exchange rate forecasting with optimum singular spectrum analysis. Journal of Systems Science and Complexity, 27(1), 47–55. https://doi.org/10.1007/s11424-014-3303-6
Ginting, A. M. (2017). ANALISIS PENGARUH EKSPOR TERHADAP PERTUMBUHAN EKONOMI INDONESIA. Buletin Ilmiah Litbang Perdagangan, 11(1), 1–20. https://doi.org/https://doi.org/10.30908/bilp.v11i1.185.
Golyandina, N. E., Alexandrov, T., & Golyandina, N. (2005). Automatic extraction and forecast of time series cyclic com-ponents within the framework of SSA Singular Spectrum Analysis View project Automatic extraction and forecast of time series cyclic components within the framework of SSA. https://www.researchgate.net/publication/237743635.
Golyandina, N., & Korobeynikov, A. (2014). Basic Singular Spectrum Analysis and forecasting with R. Computational Statistics and Data Analysis, 71, 934–954. https://doi.org/10.1016/j.csda.2013.04.009
Golyandina, N., & Zhigljavsky, A. (2013). Singular Spectrum Analysis for Time Series. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34913-3.
Hassani, H., Heravi, S., & Zhigljavsky, A. (2009). Forecasting European industrial production with singular spectrum analysis. International Journal of Forecasting, 25(1), 103–118. https://doi.org/10.1016/j.ijforecast.2008.09.007.
Hassani, H., Soofi, A. S., & Zhigljavsky, A. A. (2010). Predicting daily exchange rate with singular spectrum analysis. Nonlinear Analysis: Real World Applications, 11(3), 2023–2034. https://doi.org/10.1016/j.nonrwa.2009.05.008.
Hassani, H., & Zhigljavsky, A. (2009). SINGULAR SPECTRUM ANALYSIS: METHODOLOGY AND APPLICATION TO ECONOMICS DATA *. Jrl Syst Sci & Complexity, 22, 372–394.
Helpman, E., & Krugman, P. (1985). Market Structure and Foreign Trade: Increasing Returns, Imperfect Competition, and the International Economy. The MIT Press.
Jung, W. S., & Marshall, P. J. (1985). GROWTH AND CAUSALITY IN DEVELOPING COUNTRIES. Journal of Devel-opment Economics, 18(1), 1–12.
Kavoussi, R. M. (1984). Export expansion and economic growth: further empirical evidence. Journal of Development Economics, 14, 241–250.
Khan, M. A. R., & Poskitt, D. S. (2012). Moment tests for window length selection in singular spectrum analysis of short- and long-memory processes. Journal of Time Series Analysis, 34(2), 141–155. https://doi.org/10.1111/j.1467-9892.2012.00820.x.
Khan, M. A. R., & Poskitt, D. S. (2013). A Note on window length selection in singular spectrum analysis. Australian and New Zealand Journal of Statistics, 55(2), 87–108. https://doi.org/10.1111/anzs.12027.
Krueger, A. O. (1980). American Economic Association Trade Policy as an Input to Development. Source: The American Economic Review, 70(2), 288–292.
Kumar, U., & Jain, V. K. (2010). Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy, 35(4), 1709–1716. https://doi.org/10.1016/j.energy.2009.12.021.
Marques, C. A. F., Ferreira, J. A., Rocha, A., Castanheira, J. M., Melo-Gonçalves, P., Vaz, N., & Dias, J. M. (2006). Singu-lar spectrum analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth, 31(18), 1172–1179. https://doi.org/10.1016/j.pce.2006.02.061.
Menezes, R., Dionísio, A., & Hassani, H. (2012). On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries. Quarterly Review of Eco-nomics and Finance, 52(4), 369–384. https://doi.org/10.1016/j.qref.2012.10.002
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Source: Biometrika, 75(2), 335–381.
Qu, Z. (2011). A test against spurious long memory. Journal of Business and Economic Statistics, 29(3), 423–438. https://doi.org/10.1198/jbes.2010.09153.
Srinivasan, T. N., & Bhagwati, J. N. (1979). Trade Policy and Development. In International Economic Policy: Theory and Evidence (1st ed., pp. 1–38). Johns Hopkins University Press. https://www.researchgate.net/publication/248145710.
Suhartono, Isnawati, S., Salehah, N. A., Prastyo, D. D., Kuswanto, H., & Lee, M. H. (2018). Hybrid SSA-TSR-ARIMA for water demand forecasting. International Journal of Advances in Intelligent Informatics, 4(3), 238–250. https://doi.org/10.26555/ijain.v4i3.275.
Sulandari, W., Subanar, Suhartono, Utami, H., Lee, M. H., & Rodrigues, P. C. (2020). Ssa-based hybrid forecasting mod-els and applications. Bulletin of Electrical Engineering and Informatics, 9(5), 2178–2188. https://doi.org/10.11591/eei.v9i5.1950.
Sumiyarti, S. (2015). APAKAH HIPOTESIS “EXPORT LED GROWTH” BERLAKU DI INDONESIA? Jurnal Ekonomi & Studi Pembangunan (JESP), 16(2), 188–199.
Thomakos, D. D., Wang, T., & Wille, T. (2002). Modeling daily realized futures volatility with singular spectrum analy-sis. In Physica A (Vol. 312). www.elsevier.com/locate/physa.
Tsay, R. S. (1986). Nonlinearity tests for time series. Biometrika, 73(2), 461–467.
Unnikrishnan, P., & Jothiprakash, V. (2020). Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall. Water Re-sources Management, 34(11), 3609–3623. https://doi.org/10.1007/s11269-020-02638-w
Wang, J., & Li, X. (2018). A combined neural network model for commodity price forecasting with SSA. Soft Computing, 22(16), 5323–5333. https://doi.org/10.1007/s00500-018-3023-2.
Wen, F., Xiao, J., He, Z., & Gong, X. (2014). Stock price prediction based on SSA and SVM. Procedia Computer Science, 31, 625–631. https://doi.org/10.1016/j.procs.2014.05.309.
Yang, B., Dong, Y., Yu, C., & Hou, Z. (2016). Singular Spectrum Analysis Window Length Selection in Processing Capac-itive Captured Biopotential Signals. IEEE Sensors Journal, 16(19), 7183–7193. https://doi.org/10.1109/JSEN.2016.2594189.
Zhang, H., Yang, Y., Zhang, Y., He, Z., Yuan, W., Yang, Y., Qiu, W., & Li, L. (2021). A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting. Neural Computing and Applications, 33(2), 773–788. https://doi.org/10.1007/s00521-020-05113-0.
Zhang, Q., Wang, B. de, He, B., Peng, Y., & Ren, M. L. (2011). Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting. Water Resources Management, 25(11), 2683–2703. https://doi.org/10.1007/s11269-011-9833-y.