Abstract—A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with fore-casting problems. Based on the proposed model, we develop two algorithms for temperature prediction. Both algorithms have the advantage of obtaining good forecasting results. Index Terms—Main-factor fuzzy time series, second-factor fuzzy time series, temperature prediction, time-invariant fuzzy time se-ries, time-variant fuzzy time series. I
Abstract — A new methodology for analysis and forecasting of time series is proposed. It directly em...
There are many approaches to improve the forecasted accuracy of model based on fuzzy time series suc...
To forecast the non-stationary data is quite difficult when compared with the stationary data time s...
A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, an...
Many forecasting models based on the concepts of fuzzy time series have been proposed in the past de...
[[abstract]]Traditional time series methods fail to forecast the problems with linguistic historical...
Fuzzy time series forecasting methods, which have been widely studied in recent years, do not requir...
Fuzzy time series forecasting methods, which have been widely studied in recent years, do not requir...
In this paper, we have proposed a new modified forecasting method based on time-variant fuzzy time s...
In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time ser...
For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regres...
The extant literature has shown that fuzzy sets can be applied to solve forecasting problems. A fuzz...
WOS: 000510523600012The extant literature has shown that fuzzy sets can be applied to solve forecast...
This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy ...
High order fuzzy time series forecasting methods are more suitable than first order fuzzy time serie...
Abstract — A new methodology for analysis and forecasting of time series is proposed. It directly em...
There are many approaches to improve the forecasted accuracy of model based on fuzzy time series suc...
To forecast the non-stationary data is quite difficult when compared with the stationary data time s...
A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, an...
Many forecasting models based on the concepts of fuzzy time series have been proposed in the past de...
[[abstract]]Traditional time series methods fail to forecast the problems with linguistic historical...
Fuzzy time series forecasting methods, which have been widely studied in recent years, do not requir...
Fuzzy time series forecasting methods, which have been widely studied in recent years, do not requir...
In this paper, we have proposed a new modified forecasting method based on time-variant fuzzy time s...
In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time ser...
For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regres...
The extant literature has shown that fuzzy sets can be applied to solve forecasting problems. A fuzz...
WOS: 000510523600012The extant literature has shown that fuzzy sets can be applied to solve forecast...
This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy ...
High order fuzzy time series forecasting methods are more suitable than first order fuzzy time serie...
Abstract — A new methodology for analysis and forecasting of time series is proposed. It directly em...
There are many approaches to improve the forecasted accuracy of model based on fuzzy time series suc...
To forecast the non-stationary data is quite difficult when compared with the stationary data time s...