# Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis
## 5 Analysis and Overview of the Most Cited Articles
### A Technical Overview of the Most Cited Articles
In this model, the antecedent part of each fuzzy rule was defined using intuitionistic interval type-2 fuzzy sets, while the consequent rested upon the TSK-type FIS. The proposed model also employed a modified density-based clustering algorithm that allowed self-evolution of the intuitionistic fuzzy rules and adjustment of the network structure. The performance was compared to various variants of type-1 and type-2 fuzzy neural networks (both recurrent and feed-forward structures) using seven well-known benchmark databases and some high-frequency financial price prediction problems. The metrics used for performance evaluation were MAE, RMSE and MAPE. Hybridisation of granular computing and bio-inspired computing for M-factors time series data forecasting was proposed by [PERSON] and [PERSON] [43]. Granular computing was employed to discretize M-factors time series data set and obtain granular intervals, which were also used to fuzzify the time series data set; on the other hand, a bio-inspired algorithm was used to adjust the lengths of the intervals (both granular and non-granular) in the UoD. The proposed method was empirically compared to 12 existing models using the mean, standard deviation, RMSE and TIC to forecast the stock index prices of various companies obtained from [[https://in.finance.yahoo.com/](https://in.finance.yahoo.com/)]([https://in.finance.yahoo.com/](https://in.finance.yahoo.com/)), accessed on 20 October 2016. [PERSON] et al. presented a method based on two primary steps [44]. First, the time series data were converted into a visibility graph (network) [50] and an initial forecasting was made using link prediction [51]. Then, the second step enhanced the initial predictions with the support of fuzzy logic (defining the fuzzy sets and fuzzy rules according to inter-relationship among the historical data). The method was applied to forecast three time series databases: the construction cost index, the Taiwan stock exchange index (TAIEX) and historical enrolment of the University of Alabama. The performance evaluation metrics used in the experiments were mean absolute difference (MAD), MAPE, symmetric mean absolute percentage error (SMAPE) and normalised root mean square error (NRMSE). [PERSON] et al. developed a hybrid air quality forecasting and early warning system consisting of a deterministic prediction module and an uncertainty analysis module [45]. On the one hand, the deterministic prediction module defined the UoD and the fuzzy sets, fuzzified the observed rules, identified the FLRs, built a trend-weighted matrix where each row was for the occurrence frequency of the FLRs and predicted the output values by multiplying the defuzzified matrix and the trend-weighted matrix. On the other hand, the uncertainty analysis module applied interval forecasting based on the deterministic prediction to forecast the uncertainty of pollution concentrations. Two air pollution datasets from two cities in the Jing-Jin-Ji region (China) were used in the experiments and the performance of the forecasting system was assessed with MAPE, MAE, RMSE, median absolute percentage error, DA, fractional bias of forecasting results, index of agreement of forecasting results, Pearson's correlation coefficient and interval forecasting average width. A probabilistic intuitionistic fuzzy time series forecasting model using support vector machine to cope with both uncertainty and non-determinism associated with real-world time series was introduced by [PERSON] et al. [46]. In addition, this work also proposed a new trend-based discretization method to determine the UoD and the number of intervals. The FLRs were identified by using the ratio trend variation of crisp observations and the mean of aggregated membership values, which were modelled through a support vector machine. The experiments were carried out over 16 benchmark time series datasets and the performance of the new method was compared to seven FTS models using RMSE, SMAPE and MAE.