TY - GEN
T1 - Performance evaluation of Bootstrap-Linear recurrent formula and Bootstrap-Vector singular spectrum analysis in the presence of structural break
AU - Sasmita, Yoga
AU - Kuswanto, Heri
AU - Prastyo, Dedy D.
AU - Otok, Bambang W.
N1 - Publisher Copyright:
© 2023 Author(s).
PY - 2023/3/17
Y1 - 2023/3/17
N2 - The Singular Spectrum Analysis (SSA) forecasting method has been used widely recently. SSA has characteristics and advantages in decomposing data into trends, oscillations, and noise, which are for forecasting. Forecasting withSSA can be done by several ways e.g. Linear Recurrent Formula (LRF), Vector, and Simultaneous. The Bootstrapping process is implemented in those forecasting methods to increase the accuracy and builds the interval forecast. This study focuses on testing the sensitivity and accuracy of the combination of the Bootstrap-LRF and Bootstrap-Vector methods. The tests are applied to data that contains structural breaks, namely Indonesia's trade monthly data (exports and imports) from 1993 to 2019. The test results show that Bootstrap-Vector has a smaller forecasting range and is more accurate than Bootstrap-LRF in long-horizon forecast. Moreover, the Bootstrap-Vector is more stable in the presence of structural break in the data, meaning that this method is less sensitive to structural changes. Meanwhile, Bootstrap-LRF is more accurate in short horizon forecast.
AB - The Singular Spectrum Analysis (SSA) forecasting method has been used widely recently. SSA has characteristics and advantages in decomposing data into trends, oscillations, and noise, which are for forecasting. Forecasting withSSA can be done by several ways e.g. Linear Recurrent Formula (LRF), Vector, and Simultaneous. The Bootstrapping process is implemented in those forecasting methods to increase the accuracy and builds the interval forecast. This study focuses on testing the sensitivity and accuracy of the combination of the Bootstrap-LRF and Bootstrap-Vector methods. The tests are applied to data that contains structural breaks, namely Indonesia's trade monthly data (exports and imports) from 1993 to 2019. The test results show that Bootstrap-Vector has a smaller forecasting range and is more accurate than Bootstrap-LRF in long-horizon forecast. Moreover, the Bootstrap-Vector is more stable in the presence of structural break in the data, meaning that this method is less sensitive to structural changes. Meanwhile, Bootstrap-LRF is more accurate in short horizon forecast.
UR - http://www.scopus.com/inward/record.url?scp=85151235213&partnerID=8YFLogxK
U2 - 10.1063/5.0109951
DO - 10.1063/5.0109951
M3 - Conference contribution
AN - SCOPUS:85151235213
T3 - AIP Conference Proceedings
BT - 8th International Conference on Research Implementation and Education of Mathematics and Science, ICRIEMS 2021
A2 - Ariyanti, Nur Aeni
A2 - Pertiwi, Kartika Ratna
A2 - Sukoco, Heru
A2 - Fauzi, Fika
A2 - Kuswandi, Paramita Cahyaningrum
PB - American Institute of Physics Inc.
T2 - 8th International Conference on Research Implementation and Education of Mathematics and Science: Transforming Science Literacy into A New Normal Digital World to Achieve Sustainable Development Goals, ICRIEMS 2021
Y2 - 27 August 2021 through 28 August 2021
ER -