The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a time-series are usually vague and do not focused on any particular time point. Therefore it is not practical to define crisp bounds of the segments...

Michael R. Berthold,* Marco Ortolani, David Patterson, Frank Höppner, Ondine Callan, Heiko Hofer University of Konstanz, 78457 Konstanz, Germany University of Palermo, Department of Electrical Engineering, Viale delle Scienze 90128 Palermo, Italy Tripos, Inc., 1699 S. Hanley Road, St. Louis, MO 64133 University of Applied Sciences, Emden, Department of Electrical Engineering and Computer Scienc...

0957-4174/$ see front matter 2009 Elsevier Ltd. A doi:10.1016/j.eswa.2009.02.057 * Corresponding author. E-mail address: [email protected] (C.H. Aladag) Fuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time series data can actually...

This paper discusses hybrid probabilistic and fuzzy set approaches to propagating randomness and imprecision in risk assessment and fuzzy time series models. Stochastic and Computational Intelligence methods, such as Probability bounds analysis, Fuzzy -levels analysis, Fuzzy random vectors, Wavelets decomposition and Wavelets Networks are combined to capture different kinds of uncertainty. The...

data with high frequency have a particular type of none stationary that is called fractional none stationary. this property causes the emergence of long-term memory in financial time series with high frequency. the existence of long-term memory in cement industry time-series is studied in this paper at first and its presence will be confirmed in a high confidence level by two tests r/s and gph....

This paper describes the optimization of the fuzzy integrators in Ensembles of ANFIS models for time series prediction, this with emphasis on its application to the prediction of Mackey-Glass time series, so this benchmark time series is used to the test of performance of the proposed ensemble architecture. We used fuzzy systems to integrate the outputs (forecasts) of each of the ANFIS models i...

Various classical techniques such as linear regression, nearest neighbor have been used in developing predictive models in the past. But the methodologies developed using fuzzy time series includes a wide array of work that requires special attention. The time series analysis has been of great importance to engineering and economy problems. In this paper, we present a brief summary of the vario...

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignore...