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 (kNN), 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.