A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches
P Singh, G Dhiman - Journal of computational science, 2018 - Elsevier
In this article, a novel M-factors fuzzy time series (FTS) forecasting model is presented,
which relies upon on the hybridization of two procedures, viz., granular computing and bio …
which relies upon on the hybridization of two procedures, viz., granular computing and bio …
A quantum approach for time series data based on graph and Schrödinger equations methods
P Singh, G Dhiman, A Kaur - Modern Physics Letters A, 2018 - World Scientific
The supremacy of quantum approach is able to solve the problems which are not practically
feasible on classical machines. It suggests a significant speed up of the simulations and …
feasible on classical machines. It suggests a significant speed up of the simulations and …
A score function-based method of forecasting using intuitionistic fuzzy time series
Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In
this paper, we propose an enhanced and versatile method of forecasting using the concept …
this paper, we propose an enhanced and versatile method of forecasting using the concept …
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 …
Fuzzy time series model based on weighted association rule for financial market forecasting
CH Cheng, CH Chen - Expert Systems, 2018 - Wiley Online Library
Fuzzy time series have been used to forecast future problems based on historical data.
However, previously fuzzy time series methods have some problems:(a) subjectively …
However, previously fuzzy time series methods have some problems:(a) subjectively …
Prediksi besaran curah hujan menggunakan metode fuzzy time series
D Desmonda, T Tursina… - JUSTIN (Jurnal Sistem …, 2018 - jurnal.untan.ac.id
ClusFuDE: Forecasting low dimensional numerical data using an improved method based on automatic clustering, fuzzy relationships and differential evolution
In this paper, a novel hybrid model for forecasting low dimensional numerical data is
proposed which is named as ClusFuDE. The proposed method uses an improved automatic …
proposed which is named as ClusFuDE. The proposed method uses an improved automatic …
Modeling and long-term forecasting demand in spare parts logistics businesses
In order to provide high service levels, companies competing in the electronics
manufacturing sector need to ensure the availability of spare parts for repair and …
manufacturing sector need to ensure the availability of spare parts for repair and …
A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm
S Xian, J Zhang, Y Xiao, J Pang - Soft Computing, 2018 - Springer
Recently, many forecasting methods have been proposed for the analysis of fuzzy time
series. The main factors that affect the results of the forecasting of these models are partition …
series. The main factors that affect the results of the forecasting of these models are partition …
A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series.
SS Gautam, SR Singh - New Mathematics & Natural …, 2018 - search.ebscohost.com
Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In
this paper, we propose an enhanced and versatile method of forecasting using the concept …
this paper, we propose an enhanced and versatile method of forecasting using the concept …