Fuzzy information granules in time series data

Fuzzy information granules in time series data

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...

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

Fuzzy clustering of time series data: A particle swarm optimization approach

With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because o...

From Data to Information Granules

Residual analysis using Fourier series transform in Fuzzy time series model

In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...