In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but al...

With the capability of dealing with vague and incomplete data, the study of fuzzy time series has attracted great interest and is expected to expand rapidly. Song and Chissom (1993) first proposed the seven-step forecasting framework of fuzzy time series which are composed of (1) definition of the universe of discourse, (2) partitioning of the universe of discourse, (3) definition of fuzzy sets...

In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but al...

The present study has applied a time-invariant fuzzy time series model for maize Production in India. Most of the data is forecasted using AR and ARIMA models but considered Chen to understand endeavour forecast. Generally, are based on uncertainty, non-probabilistic linguistic variables. production India during 1951-2020 was divided into seven subsets. subsets equal intervals. Chen's used arit...

In this work we will explore the theoretical connections existing between fuzzy rule-based systems (FRBS) applied on univariate time series and two statistical reference tools, the autoregressive (AR) models and the smooth transition autoregressive (STAR) model. We will show that a TSK fuzzy rule happens to be a localised AR model and that a STAR model can hence be interpreted as a restricted F...

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rulebased models. From these relations, we derive a Lagrange multiplier linearity test and some properties of the maximum likelihood estima...

The fast and accurate forecasting method can help makers to make appropriate strategy. Zadeh was given the definition of a fuzzy set in 1965. Song and Chissom proposed the definition and the forecasting framework of fuzzy time series in 1993. Sullivan and Woodall first proposed the forecasting method to handle one factor with probability Markov model in 1994. Li and Cheng proposed a stochastic ...