Identifying secondary series for stepwise common singular spectrum analysis
Common singular spectrum analysis is a technique which can be used to forecast a primary time series by using the information from a secondary series. Not all secondary series, however, provide useful information. A first contribution in this paper is to point out the properties which a secondary se...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Operations Research Society of South Africa (ORSSA)
2013-12-01
|
| Series: | ORiON |
| Online Access: | http://orion.journals.ac.za/pub/article/view/134 |
| Summary: | Common singular spectrum analysis is a technique which can be used to forecast a primary time series by using the information from a secondary series. Not all secondary series, however, provide useful information. A first contribution in this paper is to point out the properties which a secondary series should have in order to improve the forecast accuracy of the primary series. The second contribution is a proposal which can be used to select a secondary series from several candidate series. Empirical studies suggest that the proposal performs well. |
|---|---|
| ISSN: | 2224-0004 |