paulbuenau's SSA-Toolbox (written in Java), ported into Python. Original repository can be found at https://github.com/paulbuenau/SSA-Toolbox.
Libraries required: NumPy, SciPy
Limitations:
-
The epoch labels (or at least the number of epochs) must be specified.
(The original has the option of using a heuristic to guess the number of epochs, assuming they are equally sized.) -
The algorithm always runs SSA with respect to the covariance matrix and the mean.
(The original has the option of ignoring the mean.) -
The returned result is only the estimated mixing matrix.
(The original returns the bases, projections, and signals; nevertheless, these can be computed using the estimated mixing matrix)