In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.
A part of this paper appeared in (Duru, & Butler, 2016). Stationarity control in the FTS and neural network algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver], published under the IEEE copyright.
Duru, O. and Butler, M. (2017), "Modeling and Forecasting with Fuzzy Time Series and Artificial Neural Networks", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 12), Emerald Publishing Limited, pp. 155-180. https://doi.org/10.1108/S1477-407020170000012010Download as .RIS
Emerald Publishing Limited
Copyright © 2018 Emerald Publishing Limited