Measures of Analysis of Time Series toolkit (MATS)

Measures of Analysis of Time Series (MATS) toolkit computes a number of different measures of analysis of scalar time series (linear, nonlinear and other statistical measures). It also contains pre-processing tools (transformations and standardizations), data splitting facility, resampled data generation, and visualization facilities for the time series and the computed measures. The strength of the MATS toolkit is the simultaneous operation on both multiple time series and multiple measures, allowing a range of measure specific parameters to be set as well. Help for any action is provided in html.
A main feature of MATS is that it keeps track of two lists: the time series list containing the set of scalar time series to be analyzed, and the measure list containing the values of the selected measures computed on the time series in the current time series list. The time series list is dynamic so that time series can be deleted from the list and new time series can be added to the list by selecting specific data-related operations, i.e. loading time series files, standardizing or segmenting time series from the list, or generating resampled (surrogate) time series (left part of the main menu window). A number of various measures of different types (linear, nonlinear, others) can be selected. The selected measures are then computed on all the time series in the current time series list and the measure values are stored in the current measure list, where for each measure and for each combination of its parameter values a unique name is assigned (right part of the main menu window). The computed measures can be viewed in different ways by selecting measure names from the measure list and time series names from the time series list associated to the computed measures, e.g. plots of measures versus segment or surrogate time series index, or measures versus one or two varying parameters (2D and 3D plot). The visualization facility for the measure vs surrogate time series index contains also parametric and nonparametric tests.
The user can select many different measures, as well as measure specific parameters, compute them on different time series, and view the results on the different measures and time series. It should be noted that the computation for each selected measure can be slow when a large number of time series are selected or the length of the time series is large (or both). Also, some measures, such as the measure of the local linear model and the measure of the correlation dimension, require long computation time.
Examples of measures are:
- Linear: Pearson, Spearman and Kendall autocorrelation, energy in frequency bands, goodness of fit and prediction with autoregressive (AR) model.
- Nonlinear: mutual information, third order autocorrelation, approximate entropy, correlation density and correlation dimension, false nearest neighbors, algorithmic complexity, goodness of fit and prediction of local state space models (average mapping, linear mapping, regularized linear mapping).
- Other: Hjorth parameters (used in EEG analysis), estimates of long range correlation index from Rescaled/Range analysis and Detrended Fluctuation Analysis, oscillating features statistics.
The resampled times series can be generated by the following algorithms: random shuffling, Fourier Transform, Amplitude Adjusted Fourier Transform (AAFT), Iterative AAFT (IAAFT), Statically Transformed Autoregressive Process (STAP), Residual Bootstrap from AR modeling.
If you use MATS in scientific publications, please refer to the associated paper:
D. Kugiumtzis and A. Tsimpiris (2010), Measures of Analysis of Time Series (MATS): A Matlab Toolkit for Computation of Multiple Measures on Time Series Data Bases, Journal of Statistical Software, Vol. 33, Issue 5.

Free download from Shareware Connection - Measures of Analysis of Time Series (MATS) toolkit computes a number of different measures of analysis of scalar time series (linear, nonlinear and other statistical measures)