## 1 Introduction
### Research Aim and Approach  
The purpose of this paper was to propose a new forecast combination approach resulting from FCMs, ANNs, and hybrid models. This ensemble forecasting method, including the two most popular ensemble methods, the Average and the Error-based, is based on ANNs, FCMs with learning capabilities, as well as on a hybrid FCM-ANN model with different configurations, to produce an accurate non-linear time series model for the prediction of natural gas consumption. A real case study problem of natural gas consumption in Greece was performed to show the applicability of the proposed approach. Furthermore, in order to validate the proposed forecasting combination approach, a comparison analysis between the ensemble methods and an innovative machine learning technique, the long short-term memory (LSTM) algorithm (which is devoted to time series forecasting), was conducted, and the results demonstrated enough evidence that the proposed approach could be used effectively to conduct forecasting based on multivariate time series. The LSTM algorithm, as an advanced recurrent NN method, was previously used for short-term natural gas demand forecasting in Greece [70]. In that research paper, LSTM was applied in one day-ahead natural gas consumption, forecasting for the same three Greek cities, which were also examined in the case study presented in the current paper. Many similar works can be found in the literature that examine various forecast combinations in terms of accuracy and error variability but, in the present work, an innovative approach that combines FCMs, ANNS, and hybrid FCM-ANN models, producing a non-linear time series model for the prediction of natural gas consumption, was studied exclusively, contributing to the novelty of the current study. The results demonstrated in a clear way that the proposed approach had attained better accuracies than other individual models. This study justified the superiority of the selective ensemble method over combining the important features and capabilities of the models that consist of the overall approach, making it a useful tool for future work.  
The outline of the paper is as follows. Section 2 describes the material and methods of our research study; Section 2.1 describes the case study problem and refers to the datasets of natural gas demand that are used, whereas Section 2.2 presents the analyzed approaches for time series forecasting based on ANNs, FCMs with evolutionary learning algorithms, and their hybrid combinations. The most widely used ensemble methods for forecasting problems (i.e., the error-based and the simple average method) are also presented in Section 2.2. In Section 3, the proposed forecasting combination approach is described. The same Section presents the evaluation criteria, which we have used to analyze the performance of the analyzed approaches for natural gas prediction. Section 4 presents the results of simulation analysis for three different Greek cities, as well as the conducted comparative analysis of the proposed approach with other intelligent techniques. A discussion of the results highlights the main findings of the proposed ensemble forecasts approach. Section 5 summarizes the main conclusions of the paper with further discussion and suggestions about future research expansion.