Time series data is very common in our daily life. Since they are related to time,
most of them show a periodicity. The existence of this periodic in
uence leads
to our research problem, seasonal adjustment. Seasonal adjustment is generally
applied around us, especially in areas of economy and nance. Over the last few
decades, scholars around the world made a lot of contributions in this area, and
one of the latest methods is X-13ARIMA-SEATS, which is built on ARIMA models
and linear lters. On the other hand, state space modelling (abbreviated to SSM)
is also a popular method to solve this problem and researchers including J. Durbin,
S.J. Koopman and and A. Harvery have contributed a lot of work to it. Unlike
linear lters and ARIMA models, the study on SSM starts relatively late, thus it
has not been studied and developed widely for the seasonal adjustment problem.
And SSMs have a lot advantages over those ARIMA-based and lter-based methods
such as
exibility, the understandable structure and the potential to do partial
pooling, but in practice, its default decomposition result behaves bad in some cases,
such as excessively spiky trend series; on the contrary, X-13ARIMA-SEATS could
output good decomposition result for us to analyze, but it can't be tweaked or
combined as easily as generative models and behaves like a black-box. In this paper,
we shall use Bayesian inference to combine both methods' characteristics together.
Simultaneously, to show the advantage of using SSMs concretely, we shall give a
simple application in partial pooling and talk about how to apply the Bayesian
analysis to partial pooling.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/41069 |
| Date | 21 September 2020 |
| Creators | Guo, Linyi |
| Contributors | Smith, Aaron |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.002 seconds