The point-valued time series (PTS) is simply about one value in each or period of the data, but when data have two values at time, suitable called interval-valued (ITS). An example ITS daily close and open stock prices. If are typed linguistic for example, “low increase”, “medium increase” “high a fuzzy-valued being well-known as fuzzy (FTS). aim this study to compare PTS FTS models forecasting...

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...

Fuzzy time series method can be applied in predicting the situation food price development data such as rice. The position of rice a staple has resulted this commodity being one indicators economic growth. importance suppressing prices so that they are stable done by forecasting Indonesia future. research used for is average based fuzzy Markov chain and novel algorithms series. Researchers will...

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 efficient ...

in this work, the time series modeling was used to predict the tazareh coal mine risks. for this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. after extrusion of the trend, seasonali...

Time series forecasting plays an increasingly important role in modern business decisions. In today's data-rich environment, people often aim to choose the optimal model for their data. However, identifying requires professional knowledge and experience, making accurate a challenging task. To mitigate importance of selection, we propose simple reliable algorithm improve performance. Specificall...

introduction: studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in order to gain a better understanding of the climate change phenomenon, has been effective. in ad...