In this paper, a nonlinear time series model is developed for the case when underlying data are reported by LR fuzzy numbers. To end, we present three-stage nonparametric kernel-based estimation procedure center as well left and right spreads of unknown smooth function. each stage, Nadaraya–Watson estimator used to evaluate A hybrid algorithm proposed estimate optimal bandwidths autoregressive ...

In a forecasting using historical data available in the field there is often violation of assumptions required by each method. So that are obstacles fulfillment these assumptions. Because method with Fuzzy Time Series (FTS) solution to do without requiring The purpose this study find out how results prediction highest stock price Bank Syariah Indonesia FTS lee model. This research uses value sh...

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improving forecasting accuracy, however, the issue of partitioning intervals has rarely been investigated. Recently, we proposed a novel deterministic forecasting model to eliminate the major overhead of d...

This paper proposes a high order multiple attribute fuzzy time series model, which corporate a clustering method and order weighted averaging (OWA) operator. The proposed model can deal with (1) lacking persuasiveness in determining the universe of discourse and the linguistic length of intervals, and (2) only one attribute is usually considered in forecasting not multiple attributes. Furthermo...