State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export
Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. On th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/6/1/9 |
| _version_ | 1827306191300067328 |
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| author | Yoga Sasmita Heri Kuswanto Dedy Dwi Prastyo |
| author_facet | Yoga Sasmita Heri Kuswanto Dedy Dwi Prastyo |
| author_sort | Yoga Sasmita |
| collection | DOAJ |
| description | Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. On the other hand, time-series data often exhibit multiple frequency components, such as trends, seasonality, cycles, and noise. These frequency components can be optimized in forecasting using Singular Spectrum Analysis (SSA). Furthermore, the two most widely used approaches in SSA are Linear Recurrent Formula (SSAR) and Vector (SSAV). SSAV has better accuracy and robustness than SSAR, especially in handling structural breaks. Therefore, this research proposes modeling the SSAV coefficient with an SDM approach to take structural breaks called SDM-SSAV. SDM recursively updates the SSAV coefficient to adapt over time and between states using an Extended Kalman Filter (EKF). Empirical results with Indonesian Export data and simulation studies show that the accuracy of SDM-SSAV outperforms SSAR, SSAV, SDM-SSAR, hybrid ARIMA-LSTM, and VARI. |
| first_indexed | 2024-04-24T18:17:17Z |
| format | Article |
| id | doaj.art-c0498a5d7ee146e38b24faad86136130 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-04-24T18:17:17Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-c0498a5d7ee146e38b24faad861361302024-03-27T13:41:23ZengMDPI AGForecasting2571-93942024-02-016115216910.3390/forecast6010009State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian ExportYoga Sasmita0Heri Kuswanto1Dedy Dwi Prastyo2Badan Pusat Statistik (BPS-Statistics Indonesia), Jakarta 10710, IndonesiaDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaStandard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. On the other hand, time-series data often exhibit multiple frequency components, such as trends, seasonality, cycles, and noise. These frequency components can be optimized in forecasting using Singular Spectrum Analysis (SSA). Furthermore, the two most widely used approaches in SSA are Linear Recurrent Formula (SSAR) and Vector (SSAV). SSAV has better accuracy and robustness than SSAR, especially in handling structural breaks. Therefore, this research proposes modeling the SSAV coefficient with an SDM approach to take structural breaks called SDM-SSAV. SDM recursively updates the SSAV coefficient to adapt over time and between states using an Extended Kalman Filter (EKF). Empirical results with Indonesian Export data and simulation studies show that the accuracy of SDM-SSAV outperforms SSAR, SSAV, SDM-SSAR, hybrid ARIMA-LSTM, and VARI.https://www.mdpi.com/2571-9394/6/1/9singular spectrum analysis vectorstructural breaksstate-dependent modelIndonesian export |
| spellingShingle | Yoga Sasmita Heri Kuswanto Dedy Dwi Prastyo State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export Forecasting singular spectrum analysis vector structural breaks state-dependent model Indonesian export |
| title | State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export |
| title_full | State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export |
| title_fullStr | State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export |
| title_full_unstemmed | State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export |
| title_short | State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export |
| title_sort | state dependent model based on singular spectrum analysis vector for modeling structural breaks forecasting indonesian export |
| topic | singular spectrum analysis vector structural breaks state-dependent model Indonesian export |
| url | https://www.mdpi.com/2571-9394/6/1/9 |
| work_keys_str_mv | AT yogasasmita statedependentmodelbasedonsingularspectrumanalysisvectorformodelingstructuralbreaksforecastingindonesianexport AT herikuswanto statedependentmodelbasedonsingularspectrumanalysisvectorformodelingstructuralbreaksforecastingindonesianexport AT dedydwiprastyo statedependentmodelbasedonsingularspectrumanalysisvectorformodelingstructuralbreaksforecastingindonesianexport |