| Autor: |
Kangbok Lee, Yeasung Jeong, Sunghoon Joo, Yeo Song Yoon, Sumin Han, Hyeoncheol Baik |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Machine Learning with Applications, Vol 10, Iss , Pp 100420- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2666-8270 |
| DOI: |
10.1016/j.mlwa.2022.100420 |
| Popis: |
Outliers in financial time series data are different from that in cross-sectional data in terms of the treatment and the detection. First, outliers in time series can be the focus of analysis itself, such as outliers in margin debt to indicate an overheating market. Second, the outlier detection in time series should be accompanied by decomposition to exclude inherent patterns. Unfortunately, there is a lack of consensus on the best decomposition method. Thus, we propose an ensemble model that combines multiple decomposition methods. Using the approach, we found that the outliers in margin debt are strong predictors of a recession. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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