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...

  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...

Abstract: In 2007, in Kazakhstan, there was a transition of TDM (Time Division Multiplexing) circuit-switched technologies to IP (Internet Protocol) packet technology, which created modern infrastructure for the ICT (information communication technologies) sphere. The advent IoT Things) concept has led growth functioning network at faster rate. It is currently developing direction cognitive inf...

In this paper, the authors develop a model to estimate future performance of construction projects. For the purpose of estimation, fuzzy times series models are used as an effective approach in estimation process. Furthermore, linguistic terms are applied to interpret the fuzzy-based results. The proposed model can assists project managers to develop their knowledge concerning the future aspect...

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 ...