Exponential smoothing algorithms are very attractive for the practical world
such as in industry. When considering bivariate exponential smoothing
methods, in addition to the properties of univariate methods, additional
properties give insight to relationships between the two components of a
process, and also to the overall structure of the model.
It is important to study these properties, but even with the merits the
bivariate exponential smoothing algorithms have, exponential smoothing
algorithms are nonstatistical/nonstochastic and to study the properties within
exponential smoothing may be worthless.
As an alternative approach, the (bivariate) ARIMA and the structural models
which are classes of statistical models, are shown to generalize the exponential
smoothing algorithms. We study these properties within these classes as they
will have implications on exponential smoothing algorithms.
Forecast properties are studied using the state space model and the Kalman
filter. Comparison of ARIMA and structural model completes the study. / Mathematical Sciences / M. Sc. (Statistics)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:umkn-dsp01.int.unisa.ac.za:10500/17705 |
| Date | 11 1900 |
| Creators | Seeletse, Solly Matshonisa |
| Contributors | Markham, Roger, 1941- |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (vii, 230 leaves) |
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