Browsing by Author "Kingwill, Russell"

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    Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices
    (Stellenbosch : Stellenbosch University, 2019-04) Kingwill, Russell; Brink, Willie; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    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.