Utilizing a temperature time-series prediction model to achieve good results can help us accurately sense the changes occurring in levels advance, which is important for human life. However, random fluctuations time series reduce accuracy of model. Decomposing data prior performing effectively influence and consequently improve results. In present study, we propose that combines seasonal-trend ...

In the field of climate prediction, regimes are used to model long-term cyclic trends. Although air pressure regimes have been discovered, there has been little exploration into the possibility of temperature regimes. This paper develops an approach to finding regimes in a temperature time series. First, the time series is reconstructed in a phase space. Then, a clustering algorithm is used to ...

Temperature is one of the main climatic elements that can indicate climate change as climate change seems to be one of the most important issues in the recent two decades. The aim of this research is to study temporal variation in temperature over Dibrugarh city, Assam, India during the period 1981–2010. In this article we are interested in the time series modeling of the average monthly mean t...

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...

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 forecasting probl...