Abstracts in English, Afrikaans and Northern Sotho / More time series forecasting methods were researched and made available in recent
years. This is mainly due to the emergence of machine learning methods which also
found applicability in time series forecasting. The emergence of a variety of methods
and their variants presents a challenge when choosing appropriate forecasting methods.
This study explored the performance of four advanced forecasting methods: autoregressive
integrated moving averages (ARIMA); artificial neural networks (ANN); support
vector machines (SVM) and regression models with ARIMA errors. To improve their
performance, bagging was also applied. The performance of the different methods was
illustrated using South African air passenger data collected for planning purposes by
the Airports Company South Africa (ACSA). The dissertation discussed the different
forecasting methods at length. Characteristics such as strengths and weaknesses and
the applicability of the methods were explored. Some of the most popular forecast accuracy
measures were discussed in order to understand how they could be used in the
performance evaluation of the methods.
It was found that the regression model with ARIMA errors outperformed all the other
methods, followed by the ARIMA model. These findings are in line with the general
findings in the literature. The ANN method is prone to overfitting and this was evident
from the results of the training and the test data sets. The bagged models showed mixed
results with marginal improvement on some of the methods for some performance measures.
It could be concluded that the traditional statistical forecasting methods (ARIMA and
the regression model with ARIMA errors) performed better than the machine learning
methods (ANN and SVM) on this data set, based on the measures of accuracy used.
This calls for more research regarding the applicability of the machine learning methods
to time series forecasting which will assist in understanding and improving their
performance against the traditional statistical methods / Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die
ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse.
Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes.
Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes:
outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale
netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute.
Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie
van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata
wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is.
Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel
die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is
uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te
evalueer.
Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier
metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na
oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig.
Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige
prestasiemaatstawwe vir party metodes marginaal verbeter.
Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan
die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes
(ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die
masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere
navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om
hul prestasie vergeleke met dié van die tradisionele metodes te verbeter. / Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le
go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a
le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e
ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta
ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya
maleba ya go akanya.
Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e
gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo
(ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo
(SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go
kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe.
Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya
banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo
ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se
ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go
swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a
mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go
kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye.
Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile
mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana
le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme
se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa
ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di
nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga
mešomo.
Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le
mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala
mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa
tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore
go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka
metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go
kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya
dipalopalo. / Decision Sciences / M. Sc. (Operations Research)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/27410 |
| Date | 02 1900 |
| Creators | Riba, Evans Mogolo |
| Contributors | Jankowitz, M. D. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (viii, 92 leaves, 3 unnumbered leaves) : illustrations, color graphs, application/pdf |
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