# 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  
Using the list of papers given in Table 6, this section highlights some technical subjects, including the proposed development, the time series data used in the experiments and the metrics applied to evaluate the performance in each article. [PERSON] et al. proposed a hybrid model based on FTS and convolutional neural networks for short-term load forecasting [39], in which multi-variate time series were converted into multi-channel images to be processed by the deep neural network and FTS was used to apply regularization in the input layer to decrease the effect of overfitting. The algorithm was applied to temperature time series data and hourly load data of the power supply company of the city of Johor in Malaysia in the years 2009 and 2010. The metrics used to evaluate the performance of the model were absolute percentage error (APE), mean absolute percentage error (MAPE), root mean squared error (RMSE) and median relative absolute error (MdRAE). [PERSON] et al. developed a three-stage system for wind speed time series, which comprised a data preprocessing module, an optimisation module and a forecasting module [40]. In the data preprocessing phase, ensemble empirical mode decomposition [49] was used to split the wind speed data into several intrinsic mode functions, eliminate the highest-frequency signal to reduce volatility and assemble the remaining signals to provide a new time series for forecasting. A multi-objective differential evolution algorithm and weighted FTS were applied in the optimisation and forecasting modules to achieve both accurate and stable results. The experiments were carried out over three short-term wind speed datasets from local wind farms in Penglai (China) and the performance was evaluated with mean absolute error (MAE), RMSE, MAPE, Theil's inequality coefficient (TIC), variance of the forecasting error and direction accuracy of forecasting results (DA). This work presented compared different optimization algorithms (particle swarm optimisation, cuckoo search, harmony search and firefly algorithm) and several prediction models (three different neural network architectures, support vector regression, ARIMA and double exponential smoothing). In addition, hypothesis testing was used to verify statistically significant differences between the proposed forecasting system and the other models. In the article by [PERSON] and [PERSON], a hybrid model based on fractal theory and fuzzy logic was applied to COVID-19 time series forecasting [41]. The model was built with fuzzy \begin{table}
\begin{tabular}{l c c c c} \hline
**Article** & **Citations** & \(W(i)\) & **Authors** & **Research Area** \\ \hline
[39] & 80 & 26.67 & 4 & Thermodynamics; Energy and Fuels \\
[40] & 73 & 24.33 & 3 & Energy and Fuels; Engineering \\
[41] & 46 & 23.00 & 2 & Mathematics; Physics \\
[42] & 51 & 17.00 & 4 & Computer Science \\
[43] & 66 & 16.50 & 2 & Computer Science \\
[44] & 78 & 15.60 & 3 & Mathematics \\
[45] & 54 & 13.50 & 3 & Computer Science \\
[46] & 12 & 12.00 & 3 & Automation and Control Systems; Computer Science; Engineering \\
[47] & 44 & 11.00 & 2 & Environmental Sciences, Ecology \\
[48] & 32 & 10.67 & 4 & Automation \& Control Systems; Computer Science \\ \hline \end{tabular}
\end{table} 
Table 6: List of the most influential articles. rules that employed the fractal dimensions as input values and produced the predictions. The approach was evaluated over time series data related to confirmed, recovered and fatal cases of COVID-19 from 22 January 2020 to 31 March 2020 in 10 countries, taken from the Humanitarian Data Exchange website. The only metric used to assess the performance of the model was the forecasting error (the difference between the actual and predicted values). [PERSON] et al. introduced an evolving recurrent interval type-2 intuitionistic fuzzy neural network for time series forecasting and regression [42].