HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB
Abstract
The observations of some real time series such as temperature and stock market can take different
values in a day. Instead of representing the observations of these time series by real numbers, employing
linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have
been introduced to analyze time series consisting of observations which are fuzzy sets and such time
series are called fuzzy time series. In this study, a novel approach is proposed to analyze high order
fuzzy time series model. The proposed method is applied to IMKB data and the obtained results are
discussed. IMKB data is also analyzed by using some other fuzzy time series methods available in the
literature and obtained results are compared to results obtained from the proposed method. As a result
of the comparison, it is seen that the proposed method produce accurate forecasts.
values in a day. Instead of representing the observations of these time series by real numbers, employing
linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have
been introduced to analyze time series consisting of observations which are fuzzy sets and such time
series are called fuzzy time series. In this study, a novel approach is proposed to analyze high order
fuzzy time series model. The proposed method is applied to IMKB data and the obtained results are
discussed. IMKB data is also analyzed by using some other fuzzy time series methods available in the
literature and obtained results are compared to results obtained from the proposed method. As a result
of the comparison, it is seen that the proposed method produce accurate forecasts.
Keywords
References
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