Intuitionistic fuzzy sets introduced by Atanassov are generalization of fuzzy sets as they also handle the nondeterminacy which is caused by degree of hesitation of decision maker. The present study proposes a computational method of forecasting for fuzzy time series. In the proposed method the notion of intuitionistic fuzzy set is used in fuzzy time series forecasting with simplified computati...

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...

Foreign exchange rate is a chaotic time series which is consistent with the MackeyGlass equation. Fuzzy logic is an intelligent computational technique and has good potential in forecasting time-series data. This study uses fuzzy logic to study data of exchange rates and build a dynamic adaptive neuron-fuzzy logic forecasting model. The performance of the model built is compared with an autoreg...

  In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...

Forecasting the unit cost of every product type in a factory is an important task. However, it is not easy to deal with the uncertainty of the unit cost. Fuzzy collaborative forecasting is a very effective treatment of the uncertainty in the distributed environment. This paper presents some linear fuzzy collaborative forecasting models to predict the unit cost of a product. In these models, the...

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relat...

Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large geographical region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular t...

Fuzzy time series forecasting methods has got more and more attention in recent years since they have a good capability of forecasting real-world time series which contains uncertainty. There have been various fuzzy time series forecasting methods in the literature. On the other hand, just a few ones have been proposed to forecast seasonal time series. When a seasonal time series is forecasted,...

Fuzzy neural networks have been successfully applied to generate predictive rules for stocks forecasting. This paper presents a methodology for forecasting S&P 500 index based on the neural network with weighted fuzzy membership functions (NEWFM) and time series of S&P 500 index based on the defuzzyfication of weighted average method (The fuzzy model suggested by Takagi and Sugeno in 1985). NEW...

Forecasting of petroleum production time series is a key task underlying scheduling of oil refinery production. In turn, forecasting requires analysing whether time series exhibits chaotic behavior. In this paper we consider chaos analysis based forecasting of time series of gasoline and diesel production. Chaos analysis is based on Lyapunov exponents and includes determination of optimal value...