fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal...

Researchers introduce in this paper, an efficient fuzzy time series forecasting model based on fuzzy clustering to handle forecasting problems and improving forecasting accuracy. Each value (observation) is represented by a fuzzy set. The transition between consecutive values is taken into account in order to model the time series data. Proposed model employed eight main steps in time-invariant...