Chapter Introduction |
Module 29.1: nag_tsa_identify - Time Series Analysis - Identification |
nag_tsa_acf |
Calculates the sample autocorrelation function of
a univariate time series |
nag_tsa_pacf |
Calculates the sample partial autocorrelation function of a univariate time series |
Examples
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Module 29.2: nag_tsa_kalman - Kalman Filtering |
nag_kalman_init |
Provides an initial estimate of the Kalman filter state covariance matrix |
nag_kalman_predict |
Calculates a one step prediction for the square root covariance Kalman filter |
nag_kalman_sqrt_cov_var |
Calculates a time-varying square root covariance Kalman filter |
nag_kalman_sqrt_cov_invar |
Calculates a time-invariant square root covariance Kalman filter |
Examples
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Module 29.3: nag_tsa_spectral - Time Series Spectral Analysis |
nag_spectral_data |
Calculates the smoothed sample spectrum of a univariate time series |
nag_spectral_cov |
Calculates the smoothed sample spectrum of a univariate time series using autocovariances data |
nag_bivar_spectral_data |
Calculates the smoothed sample cross spectrum of a bivariate time series |
nag_bivar_spectral_cov |
Calculates the smoothed sample cross spectrum of a bivariate time series using autocovariances data |
nag_bivar_spectral_coh |
Calculates the squared coherency, the cross amplitude, the gain and the phase spectra |
nag_bivar_spectral_lin_sys |
Calculates the noise spectrum and the impulse response function from a linear system |
Examples
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