# Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis
## 2 Some Related Reviews  
The extensive twenty-year (1993-2013) review on FTS modelling procedures carried out by [PERSON] constitutes one of the most thorough works [19]. The paper concluded that significant research on FTS modelling was addressed to design algorithms for the discretization of time series data, to generate rules from the fuzzified time series values, propose techniques for defuzzification and develop hybridised-based architectures to resolve complex decision-making problems. An excellent review on linear and non-linear time series analysis and forecasting was presented by [PERSON] and [PERSON] [2], summarizing works published between 1982 and 2005 with especial emphasis on exponential smoothing, ARIMA and state-space and structural models. Despite the vast amount of papers, this review included only one study based on a fuzzy system to combine a set of individual forecasts. [PERSON] presented a survey on the design of model-based fuzzy control systems focusing on the Takagi-Sugeno fuzzy models [20]. [PERSON] and [PERSON] performed a systematic review of the literature on fuzzy information granules, which need to achieve a trade-off between interpretability and accuracy [21]. [PERSON] et al. presented a survey on a collection of methods to predict the water-level fluctuation in lakes, including ANFIS and hybrid wavelet-ANFIS [22]. [PERSON] et al. reviewed the state of the art of classical and deep learning models for time series forecasting that were designed to capture temporal relationships explicitly, and categorized them into three groups [23]: discriminative, generative and hybrid models. The authors also described the pros and cons of each model, including a comparison between classical (e.g., least-square support vector machine, shallow neural networks, Bayesian networks, fuzzy C-means) and deep learning (e.g., long short-term memory, auto-encoder, restricted Boltzmann machine, generative adversarial nets) fuzzy-based time series models. In addition, the paper gives a set of experiments on both benchmarks and real-world data to evaluate the performance of the most representative deep learning models. [PERSON] et al. focused their review on methods and software for fuzzy cognitive maps applied to time series forecasting [24]. A review on the state-of-the-art applications of interval type-2 fuzzy neural networks to chaotic time series forecasting was carried out by [PERSON] et al., summarizing the main contributions and some hardware implementations for speeding up computation [25]. A comprehensive review of machine learning techniques for forecasting time series' energy consumption was presented by [PERSON] et al. [26]; although this work was not explicitly focused on fuzzy models, the authors included an extensive part to address FTS prediction and modelling. [PERSON] and [PERSON] performed a meta-analysis in a literature review from  
Figure 1: A taxonomy of FTS models [18]. 2000 to 2015, covering 86 empirical studies on the time series modelling and forecasting of electricity spot prices [27]. [PERSON] and [PERSON] examined more than 150 papers based on three different hybrid structures (parallel, series, and parallel-series) for time series modelling and forecasting, concluding that the parallel-series hybrid structure performed better than the other two hybrid models [28]. [PERSON] and [PERSON] summarised and reviewed the contributions to FTS forecasting in the time period 1993-2018, published in Elsevier journals [4]; in addition, this work provided a list of different error estimation metrics, major application areas and the most used databases in the topic of fuzzy models for time series forecasting. [PERSON] et al. provided an in-depth review of the optimal design of type-1 and type-2 FIS using five computational frameworks [29]: genetic fuzzy systems, neuro-fuzzy systems, hierarchical fuzzy systems, evolving fuzzy systems and multi-objective fuzzy systems. [PERSON] et al.