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A Conditional Fuzzy Inference Approach in Forecasting

Hassanniakalager, Arman and Sermpinis, Georgios and Stasinakis, Charalampos and Verousis, Thanos (2020) 'A Conditional Fuzzy Inference Approach in Forecasting.' European Journal of Operational Research, 283 (1). 196 - 216. ISSN 0377-2217

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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.

Item Type: Article
Uncontrolled Keywords: Classification, Conditional Fuzzy Inference, Forecasting, Fuzzy Rules
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 15 Nov 2019 11:09
Last Modified: 04 Feb 2020 15:15
URI: http://repository.essex.ac.uk/id/eprint/25783

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