kSpectra is a scientific application for advanced time series analysis. It 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 life, biomedical sciences, finance and economics. kSpectra helps to identify and predict periodic signals in noisy and short and gappy time series, when standard methods (FFT/wavelets etc) have limited applicability. kSpectra contains procedures for:


- 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.