Traditional forecasting methods need strict assumptions such as normality and linearity. It is very difficult to satisfy these assumptions for real-world time series. Many realworld time series can be easily analyzed by using fuzzy time series methods since fuzzy time series methods do not require any strict assumptions. Therefore, fuzzy time series approaches have been getting more and more at...

Researchers introduce in this paper, an efficient fuzzy time series forecasting model based on fuzzy clustering to handle forecasting problems and improving forecasting accuracy. Each value (observation) is represented by a fuzzy set. The transition between consecutive values is taken into account in order to model the time series data. Proposed model employed eight main steps in time-invariant...

Most of today’s time series data contain anomalies and multiple seasonalities, accurate anomaly detection in these is critical to almost any type business. However, most mainstream forecasting models used for can only incorporate one or no seasonal component into their forecasts cannot capture every known pattern data. In this paper, we propose a new multi-seasonal model that extends the popula...

thunderstorms are climatical phenomena, which sometimes can cause irreparable damage to the ecology. in this study to identify any trends in the frequency of days with thunderstorms in iran, long-term statistics of 33 synoptic stations, from the beginning until 2005 was used. at first by applying nonparametric man-kendall and sen’s estimator methods on four seasonal time series, and one annua...

We propose a new, human consistent method for the evaluation of similarity of time series that uses a fuzzy quantifier base aggregation of trends (segments), within the authors’ (cf. Kacprzyk, Wilbik, Zadrożny [1, 2, 3, 4, 5, 6] or Kacprzyk, Wilbik [7, 8, 9]) approach to the linguistic summarization of trends based on Zadeh’s protoforms and fuzzy logic with linguistic quantifiers. The results o...