Financial Time Series Analysis Based on Normalized Mutual Information Functions

Financial Time Series Analysis Based On Normalized Mutual Information Functions

A method of predictability analysis of future values of financial time series is described. The method is based on normalized mutual information functions. In the analysis, the use of these functions allowed to refuse any restrictions on the distributions of the parameters and on the correlations between parameters. A comparative analysis of the predictability of financial time series of Tel Av...

3D Registration based on Normalized Mutual Information

Medical image registration is time-consuming but can be sped up employing parallel processing on the GPU. Normalized mutual information (NMI) is a well performing similarity measure for performing multi-modal registration. We present CUDA based solutions for computing NMI on the GPU and compare the results obtained by rigidly registering multi-modal data sets with a CPU based implementation. Ou...

Frequency-based Analysis of Financial Time Series

Analysis of fMRI time series with mutual information

Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Stati...

Interpolating time series based on fuzzy cluster analysis problem

This study proposes the model for interpolating time series to use them  to forecast effectively for future. This model is established based on the improved fuzzy clustering analysis problem, which is implemented by the Matlab procedure. The proposed model is illustrated by a data set and tested for many other datasets, especially for 3003 series in  M3-Competition data. Comparing  to the exist...