Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices
| dc.contributor.advisor | Brink, Willie | en_ZA |
| dc.contributor.author | Kingwill, Russell | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics. | en_ZA |
| dc.date.accessioned | 2019-02-26T08:55:21Z | |
| dc.date.accessioned | 2019-04-17T08:15:07Z | |
| dc.date.available | 2019-02-26T08:55:21Z | |
| dc.date.available | 2019-04-17T08:15:07Z | |
| dc.date.issued | 2019-04 | |
| dc.description | Thesis (MSc)--Stellenbosch University, 2019. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT : South Africa has a number of fuel grades available to consumers, one of the most popular being the 95 unleaded standard. The price of this fuel is comprised of many components including transport fees, taxes and the basic fuel price. The basic fuel price is the cost in Rand of Brent crude oil used to re ne the unit of petrol fuel, and is often the most signi cant component of the fuel price as well as the most volatile. Having a reliable forecasting methodology for the basic fuel price would be a helpful planning tool for many individuals and small enterprises. The forecasting of general fuel prices has been studied in the past with various forecasting techniques ranging from machine learning to ARIMA and regression models. In this study various deep learning models, including feed forward, recurrent and convolutional neural networks are assessed for their ability to accurately forecast the basic fuel price. These models are ranked by their ability to reduce the mean absolute percentage error on a common test data set. A number of time series data sets are used as input for the models under review, which include the closing daily price of Brent crude oil and the closing daily US Dollar exchange rate. The e ect of inputting the 30 day rolling future contracts for both the closing oil price and exchange rates is also investigated. Overall it is determined that, of the models evaluated during this study, the recurrent network performs the most favourably. On the nal test set, with optimal model and input parameters, the individual observation errors range from less than 1 % to more than 10 %. The average test error of 4.57 % can be a bit misleading due to the observed range of individual errors. Hence it is not as reliable of a forecast as one would hope for. However, the model did prove to have a fairly reliable attribute to correctly forecast the direction of the basic fuel price change. It did so in about 86% of the test data set observations, and was o by only a few cents when an incorrect direction was forecast. It is concluded that neural network models can be used to some degree for the task of forecasting the South African basic fuel price. Such models are sensitive to the amount of data provided and hence future work in this area should prioritise obtaining more data and if possible incorporating additional data sources. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : Suid-Afrika het 'n aantal brandstofgrade, waarvan een van die gewildste die 95-loodvrye standaard is. Hierdie brandstof se prys bestaan ondermeer uit vervoerfooie, belasting en die basiese brandstofprys. Die basiese brandstofprys is die koste in Rand van Brent-ru-olie wat vir verfyning gebruik word, en is dikwels die belangrikste komponent van die brandstofprys sowel as die mees wisselvallige. Om 'n betroubare voorspellingsmetodologie vir die basiese brandstofprys te hê, kan nuttig vir baie individue en klein ondernemings wees. Die voorspelling van algemene brandstofpryse is in die verlede bestudeer met tegnieke wat wissel van masjienleer tot ARIMA en regressiemodelle. In hierdie studie word verskeie diepleermodelle, insluitende voortvoerende, terugkerende en konvolusie neurale netwerke, beoordeel vir hul vermoë om die basiese brandstofprys akkuraat te voorspel. Hierdie modelle word gerangskik volgens hul vermoë om die gemiddelde absolute persentasie-fout op 'n algemene toetsdatastel te verminder. 'n Aantal tydreeksdatastelle is gebruik as intreë wat die sluiting van die daaglikse prys van Brent-ru-olie en van die daaglikse Amerikaanse Dollar-wisselkoers insluit. Die e ek van die insluiting van die 30 dae toekomstige kontrakte vir die sluitingsprys en wisselkoerse word ook ondersoek. Oor die algemeen is vasgestel dat van die modelle wat in hierdie studie geëvalueer is, die terugkerende netwerk die gunstigste presteer. Op die nale toetsstel, met optimale model- en insetparameters, wissel individuele foute van minder as 1 % tot meer as 10 %. Die gemiddelde fout op die toetsdatastel van 4.57 % kan 'n bietjie misleidend wees as gevolg van die verspreiding van individuele foute. Dit is dus nie so betroubaar soos wat mens sou hoop nie. Die model toon egter 'n redelike betroubare vermoë om die rigting van verandering in die basiese brandstofprys korrek te voorspel. Dit is gedoen in ongeveer 86% van die toetsdatastel waarnemings, en was af met slegs 'n paar sent toe 'n verkeerde rigting voorspel is. Daar word tot die gevolgtrekking gekom dat neurale netwerkmodelle tot 'n mate gebruik kan word om die Suid-Afrikaanse basiese brandstofprys te voorspel. Sulke modelle is sensitief vir die hoeveelheid data wat verskaf word en daarom moet toekomstige werk in hierdie gebied voorkeur gee aan die verkryging van meer data en indien moontlik die insluiting van bykomende databronne. | af_ZA |
| dc.format.extent | vii, 65 pages : illustrations (some colour) | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/105838 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Fuel prices -- Forecasting | en_ZA |
| dc.subject | Machine learning | en_ZA |
| dc.subject | Neural networks | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.subject | Brent crude oil -- Prices | en_ZA |
| dc.subject | Fuel prices -- Mathematical models | en_ZA |
| dc.title | Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices | en_ZA |
| dc.type | Thesis | en_ZA |