Abstract
M.Ing. ( Electrical & Electronic Engineering Science)
Economic modelling tools have gained popularity in recent years due to the increasing need for greater knowledge to assist policy makers and economists. A number of computational intelligence approaches have been proposed for economic modelling. Most of these approaches focus on the accuracy of prediction and not much research has been allocated to investigate the interpretability of the decisions derived from these systems.
This work proposes the use of computational intelligence techniques (Rough set theory (RST) and the Multi-layer perceptron (MLP) model) to model the South African economy. RST is a rule-based technique suitable for analysing vague, uncertain and imprecise data. RST extracts rules from the data to model the system. These rules are used for prediction and interpreting the decision process.
The lesser the number of rules, the easier it is to interpret the model. The performance of the RST is dependent on the discretization technique employed. An equal frequency bin (EFB), Boolean reasoning (BR), entropy partition (EP) and the Naïve algorithm (NA) are used to develop an RST model. The model trained using EFB data performs better than the models trained using BR and EP.
RST was used to model South Africa’s financial sector. Here, accuracy of 86.8%, 57.7%, 64.5% and 43% were achieved for EFB, BR, EP and NA respectively.
This work also proposes an ensemble of rough set theory and the multi-layer perceptron model to model the South African economy wherein, a prediction of the direction of the gross domestic product is presented. This work also proposes the use of an auto-associative Neural Network to impute missing economic data. The auto-associative neural network imputed the ten variables or attributes that were used in the prediction model.
These variables were: Construction contractors rating lack of skilled labour as constraint, Tertiary economic sector contribution to GDP, Income velocity of circulation of money, Total manufacturing production volume, Manufacturing firms rating lack of skilled labour as constraint, Total asset value of banking industry, Nominal unit labour cost, Total mass of Platinum Group Metals (PGMs) mined, Total revenue from sale of PGMs and the Gross Domestic Expenditure (GDE).
The level of imputation accuracy achieved varied with the attribute. The accuracy ranged from 85.9% to 98.7%.