| dc.contributor.author |
Ramirez, O |
en |
| dc.contributor.author |
Mullen, J |
en |
| dc.contributor.author |
Collart, AJ |
en |
| dc.date.accessioned |
2014-06-06T06:53:05Z |
|
| dc.date.available |
2014-06-06T06:53:05Z |
|
| dc.date.issued |
2014 |
en |
| dc.identifier.issn |
02664763 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1080/02664763.2014.909791 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/6349 |
|
| dc.subject |
data aggregation |
en |
| dc.subject |
efficient forecasting |
en |
| dc.title |
Insights into the appropriate level of disaggregation for efficient time series model forecasting |
en |
| heal.type |
other |
en |
| heal.identifier.primary |
10.1080/02664763.2014.909791 |
en |
| heal.publicationDate |
2014 |
en |
| heal.abstract |
This paper provides a potentially valuable insight on how to assess if the forecasts from an autoregressive moving average model based on aggregated data could be substantially improved through disaggregation. It is argued that, theoretically, the absence of moving average (MA) terms indicates that no forecasting efficiency improvements can be achieved through disaggregation. In practice, it is found that there is a strong correlation between the statistical significance of the MA component in the aggregate model and the magnitude of the forecast mean square error (MSE) decreases that can be achieved through disaggregation. That is, if a model includes significant MA terms, the forecast MSE improvements that may be gained from disaggregation could be substantial. Otherwise, they are more likely to be relatively small or non-existent. © 2014 © 2014 Taylor & Francis. |
en |
| heal.journalName |
Journal of Applied Statistics |
en |
| dc.identifier.doi |
10.1080/02664763.2014.909791 |
en |