A conditional fuzzy inference approach in forecasting

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.