A conditional fuzzy inference approach in forecasting
Hassanniakalager, Arman Sermpinis, Georgios Stasinakis, Charalampos Verousis, Thanos
Hassanniakalager, Arman
Sermpinis, Georgios
Stasinakis, Charalampos
Verousis, Thanos
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Publication Type
Journal article with impact factor
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Supervisor
Publication Year
2020
Journal
European Journal of Operational Research
Book
Publication Volume
283
Publication Issue
1
Publication Begin page
196
Publication End page
216
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Abstract
This study introduces a Conditional fuzzy inference (CF) approach in forecasting. The proposed approach is able to deduct Fuzzy Rules (FRs) conditional on a set of restrictions. This conditional rule selection discards weak rules and the generated forecasts are based only on the most powerful ones. Through this process, it is capable of achieving higher forecasting performance and improving the interpretability of the underlying system. The CF concept is applied in a series of forecasting exercises on stocks and football games datasets. Its performance is benchmarked against a Relevance Vector Machine (RVM), an Adaptive Neuro-Fuzzy Inference System (ANFIS), an Ordered Probit (OP), a Multilayer Perceptron Neural Network (MLP), a k-Nearest Neighbour (k-NN), a Decision Tree (DT) and a Support Vector Machine (SVM) model. The results demonstrate that the CF is providing higher statistical accuracy than its benchmarks.
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Keywords
Forecasting, Conditional Fuzzy Inference, Fuzzy Rules, Classification