| 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 | |
| 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 | |
| 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 | |