A conditional fuzzy inference approach in forecasting

Hassanniakalager, A., Sermpinis, G. , Stasinakis, C. and Verousis, T. (2020) A conditional fuzzy inference approach in forecasting. European Journal of Operational Research, 283(1), pp. 196-216. (doi:10.1016/j.ejor.2019.11.006)

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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:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Verousis, Dr Thanos and Stasinakis, Dr Charalampos and Hassanniakalager, Arman and Sermpinis, Professor Georgios
Authors: Hassanniakalager, A., Sermpinis, G., Stasinakis, C., and Verousis, T.
College/School:College of Social Sciences > Adam Smith Business School > Accounting and Finance
College of Social Sciences > Adam Smith Business School > Economics
Journal Name:European Journal of Operational Research
Publisher:Elsevier
ISSN:0377-2217
ISSN (Online):1872-6860
Published Online:09 November 2019
Copyright Holders:Copyright © 2019 Crown Copyright
First Published:First published in European Journal of Operational Research 283:196-216
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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