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kSpectra Toolkit consists of a set of programs for advanced spectral analysis of univariate or multivariate time series arising in many of the physical sciences, ranging from electrical engineering and physics to geophysics and oceanography, as well as life and biomedical sciences. The toolkit contains procedures for:
- estimating the spectrum, cross-spectrum and coherence,
- decomposing the time series into trends, oscillatory components, and noise,
- reconstructing and predicting the contributions of selected components of the time series.
- gap-filling technique for analysis of datasets with missing data.
For spectral analysis Blackman-Tukey correlogram estimation (BT), the Maximum-Entropy Method (MEM), the Multi-Taper Method (MTM), Singular-spectrum Analysis (SSA), Multi-Channel SSA (MSSA) and Principal Component Analysis (PCA) provided. SSA (MSSA) and MTM can also be used for separation, reconstruction and prediction of trends, near-periodic and other significant components from noise, using sophisticated statistical significance tests. The basic philosophy of the Toolkit 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. This help document briefly describes some of the theory behind the various methods, and demonstrates how to use the Toolkit. More details about each method can be found in the White Papers references.
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