Outliers detection in state-space model using indicator saturation approach
Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework....
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-06-01.
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| LEADER | 02668 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23601_15144 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Che Rose, Farid Zamani |e author |
| 100 | 1 | 0 | |a School of Mathematical Sciences, Universiti Sains Malaysia |e contributor |
| 700 | 1 | 0 | |a Ismail, Mohd Tahir |e author |
| 700 | 1 | 0 | |a Tumin, Mohd Hanafi |e author |
| 245 | 0 | 0 | |a Outliers detection in state-space model using indicator saturation approach |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23601 | ||
| 520 | |a Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework. Focusing on impulse indicator saturation, performance recorded by the suggested approach was evaluated using Monte Carlo simulations. To tackle the issue of higher number of regressors compared to the number of observations, this research utilized the split-half approach algorithm. We found that the impulse indicator saturation performance relies heavily on the size of outlier, location of outlier and number of splits in the series examined. Detection of outliers using sequential and non-sequential algorithms is the most crucial issue in this study. The sequential searching algorithm was able to outperform the non-sequential searching algorithm in eliminating the non-significant indicators based on potency and gauge. The outliers captured using impulse indicator saturation in financial times stock exchange (FTSE) United States of America (USA) shariah index correspond to the financial crisis in 2008-2009. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a computing; machine learning; time series model; | ||
| 690 | |a additive outlier; general-to-specific; indicator saturation; local level; Monte Carlo simulation; state space; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 3: June 2021; 1688-1696 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23601/15144 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23601/15144 |z Get fulltext |