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...

In this paper, we provide theoretical justification for the application of higher degree fuzzy transform in time series analysis. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and a random noise, we demonstrate that the higher degree fuzzy transform technique can be used for the estimation of the trend-cycle, which is one of the ba...

Fuzzy time series forecasting methods do not require constraints found in conventional approaches. In addition, due to uncertainty that they contain, many time series to be forecasted should be considered as fuzzy time series. Fuzzy time series forecasting models consist of three steps as fuzzification, identification of fuzzy relations and defuzzification. Although most of the time series enco...

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 time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are compatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising from seasonality. These variations are called seaso...

background: this study attempted to investigate traffic accident fatalities during 2004-2009 and the effect of fuel rationing on traffic accident fatalities .   materials and methods: this was a cross-sectional study on available data of all traffic accident fatalities in fars province, iran, during 2004-2009. to identify and fit the best model, various instruments, including the autocorrelatio...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

Several time-variant fuzzy time series models have been developed during the last decade. These models usually focus on forecasting stationary of trend time series, but they are not suitable for forecasting seasonal time series. Furthermore, several factors that affect the forecasting accuracy are not carefully examined, such as interval length, interval number, and level of window base. Aiming...

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the sugge...