Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap
The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global
health calamity that has a profound impact on the way of perceiving the world and everyday …
health calamity that has a profound impact on the way of perceiving the world and everyday …
Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting
K Benmouiza, A Cheknane - Theoretical and Applied Climatology, 2019 - Springer
In this paper, an improved clustered adaptive neuro-fuzzy inference system (ANFIS) to
forecast an hour-ahead solar radiation data for 915 h is introduced. First, we have classified …
forecast an hour-ahead solar radiation data for 915 h is introduced. First, we have classified …
A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series
Financial time series prediction problems, for decision-makers, are always crucial as they
have a wide range of applications in the public and private sectors. This study presents a …
have a wide range of applications in the public and private sectors. This study presents a …
A new CNN-based model for financial time series: TAIEX and FTSE stocks forecasting
M Kirisci, O Cagcag Yolcu - Neural Processing Letters, 2022 - Springer
Financial time series forecasting has been becoming one of the most attractive topics in so
many aspects owing to its broad implementation areas and substantial impact. Because of …
many aspects owing to its broad implementation areas and substantial impact. Because of …
A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm
E Bas, E Egrioglu - Information Sciences, 2022 - Elsevier
Fuzzy inference systems, referring to a system that works on fuzzy sets, have been used in
many areas such as classification, information order, especially in the field of forecasting …
many areas such as classification, information order, especially in the field of forecasting …
Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony
Intuitionistic fuzzy sets are extended form of type 1 fuzzy sets. The modeling methods use
intuitionistic fuzzy sets have second-order uncertainty approximation so these methods may …
intuitionistic fuzzy sets have second-order uncertainty approximation so these methods may …
A new hybrid method for time series forecasting: AR–ANFIS
B Sarıca, E Eğrioğlu, B Aşıkgil - Neural Computing and Applications, 2018 - Springer
In this study, a new hybrid forecasting method is proposed. The proposed method is called
autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be …
autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be …
High-order fuzzy time series forecasting by using membership values along with data and support vector machine
In the past few years, non-stochastic fuzzy time series (FTS) models have drawn remarkable
attention of researchers from different domains. Unlike traditional stochastic models, FTS …
attention of researchers from different domains. Unlike traditional stochastic models, FTS …
A combined robust fuzzy time series method for prediction of time series
In case of outlier (s) it is inevitable that the performance of the fuzzy time series prediction
methods is influenced adversely. Therefore, current prediction methods will not be able to …
methods is influenced adversely. Therefore, current prediction methods will not be able to …
Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process
OC Yolcu, F Alpaslan - Applied Soft Computing, 2018 - Elsevier
All fuzzy time series approaches proposed in the literature consider three steps constituting
the solution process as separate processes. Thus, model error is the sum of the errors that …
the solution process as separate processes. Thus, model error is the sum of the errors that …