kSpectra Lite 3.4

kSpectra Lite provides a set of powerful tools for analysis of univariate or multivariate time series in various sciences, ranging from electrical engineering and physics to geophysics, as well as biomedical sciences, life, finance and economics.

kSpectra Lite helps predict and identify periodic signals in noisy, short and gappy time series, when standard methods (FFT/wavelets etc) have limited applicability.

kSpectra Lite includes Blackman-Tukey Correlogram (BT-FFT), Maximum-Entropy Method (MEM), MultiTaper Method (MTM), Singular Spectrum Analysis (SSA), and Principal Component Analysis (PCA).

The basic philosophy of kSpectra Lite is that only the simultaneous and flexible application of more than one spectral estimation method can provide truly reliable information on a given time series, when the signal-to-noise ratio is low.

The results of kSpectra Lite are mainly spectrograms (power vs. frequencies) and significance tests against noise level from various spectral estimation methods (SSA, MTM, MEM, BT- FFT).

The user typically needs to import the data by using Data I/O panel into named data objects (Vectors or Matrices), and then apply different spectral estimation tools; each tool has it’s own panel of GUI. The user’s task is to interpret obtained spectrograms for presence of oscillatory modes.

NOTE: To buy kSpectra Lite via the App Store, an Apple account is required.

Here are some key features of "kSpectra Lite":

· estimating the spectrum, cross-spectrum and coherence,
· decomposing the time series into trends, oscillatory components, and noise by using sophisticated statistical significance tests,
· reconstructing and predicting the contributions of trends and near-periodic components of the time series,
· gap-filling technique for analysis of datasets with missing data.

What's New in This Release: [ read full changelog ]

· Varimax rotation in multichannel singular spectrum analysis and improved significance tests for better identification of oscillatory modes in multivariate data;
· Optimized for mountain lion.