The complete collection of python scripts for estimating and simulating time-varying Multivariate Autoregressive processes (tv-MVAR) by means of Kalman filtering and Self-Tuning Optimized Kalman filtering.
The toolbox includes:
- One ipython notebook demo (please refer to the file dynet_demo01 for a brief tutorial)
- Four scripts containing classes and functions:
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'dynet_statespace'
- dynet_SSM_KF implements the Kalman filter for state-space modeling of physiological time series
- dynet_SSM_STOK implements the Self-Tuning Optimized Kalman filter
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'dynet_con'
- dynet_ar2pdc estimates the tv PDC from tv-AR coefficients
- dynet_connplot displays connectivity matrices (function of time and frequency) for each combination of signals
- dynet_parpsd estimates the AR coefficients in the frequency domain and the parametric power spectral density of the input signals
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'dynet_sim'
- dynet_sim is the simulation class for tv-MVAR generated surrogate time series
- dynet_sim.review() displays the 1) structural adjacency matrix, 2) the functional adjacency matrix, 3)surrogate time-series in the time domain,
- the power spectral density of surrogate time-series
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'utilities' contains other functions for the demo.
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