Function |
Mark of Introduction |
Purpose |
---|---|---|
g13aac | 7 | nag_tsa_uni_diff Univariate time series, seasonal and non-seasonal differencing |
g13abc | 2 | nag_tsa_uni_autocorr Sample autocorrelation function |
g13acc | 2 | nag_tsa_uni_autocorr_part Partial autocorrelation function |
g13amc | 9 | nag_tsa_uni_smooth_exp Univariate time series, exponential smoothing |
g13asc | 6 | nag_tsa_uni_arima_resid Univariate time series, diagnostic checking of residuals, following g13bec |
g13auc | 7 | nag_tsa_uni_means Computes quantities needed for range-mean or standard deviation-mean plot |
g13awc | 25 | nag_tsa_uni_dickey_fuller_unit Computes (augmented) Dickey–Fuller unit root test statistic |
g13bac | 7 | nag_tsa_multi_filter_arima Multivariate time series, filtering (pre-whitening) by an ARIMA model |
g13bbc | 7 | nag_tsa_multi_filter_transf Multivariate time series, filtering by a transfer function model |
g13bcc | 7 | nag_tsa_multi_xcorr Multivariate time series, cross-correlations |
g13bdc | 7 | nag_tsa_multi_transf_prelim Multivariate time series, preliminary estimation of transfer function model |
g13bec | 2 | nag_tsa_multi_inputmod_estim Estimation for time series models |
g13bgc | 8 | nag_tsa_multi_inputmod_update Multivariate time series, update state set for forecasting from multi-input model |
g13bjc | 2 | nag_tsa_multi_inputmod_forecast Forecasting function |
g13bxc | 2 | nag_tsa_options_init Initialization function for option setting |
g13byc | 2 | nag_tsa_transf_orders Allocates memory to transfer function model orders |
g13bzc | 2 | nag_tsa_trans_free Freeing function for the structure holding the transfer function model orders |
g13cac | 7 | nag_tsa_uni_spectrum_lag Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
g13cbc | 4 | nag_tsa_uni_spectrum_daniell Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
g13ccc | 7 | nag_tsa_multi_spectrum_lag Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
g13cdc | 4 | nag_tsa_multi_spectrum_daniell Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
g13cec | 4 | nag_tsa_multi_spectrum_bivar Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra |
g13cfc | 4 | nag_tsa_multi_gain_bivar Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra |
g13cgc | 4 | nag_tsa_multi_noise_bivar Multivariate time series, noise spectrum, bounds, impulse response function and its standard error |
g13dbc | 7 | nag_tsa_multi_autocorr_part Multivariate time series, multiple squared partial autocorrelations |
g13ddc | 8 | nag_tsa_multi_varma_estimate Multivariate time series, estimation of VARMA model |
g13djc | 8 | nag_tsa_multi_varma_forecast Multivariate time series, forecasts and their standard errors |
g13dkc | 8 | nag_tsa_multi_varma_update Multivariate time series, updates forecasts and their standard errors |
g13dlc | 7 | nag_tsa_multi_diff Multivariate time series, differences and/or transforms |
g13dmc | 7 | nag_tsa_multi_corrmat_cross Multivariate time series, sample cross-correlation or cross-covariance matrices |
g13dnc | 7 | nag_tsa_multi_corrmat_partlag Multivariate time series, sample partial lag correlation matrices, statistics and significance levels |
g13dpc | 7 | nag_tsa_multi_regmat_partial Multivariate time series, partial autoregression matrices |
g13dsc | 8 | nag_tsa_multi_varma_diag Multivariate time series, diagnostic checking of residuals, following g13ddc |
g13dxc | 7 | nag_tsa_uni_arma_roots Calculates the zeros of a vector autoregressive (or moving average) operator |
g13eac | 3 | nag_tsa_multi_kalman_sqrt_var One iteration step of the time-varying Kalman filter recursion using the square root covariance implementation |
g13ebc | 3 | nag_tsa_multi_kalman_sqrt_invar One iteration step of the time-invariant Kalman filter recursion using the square root covariance implementation with in lower observer Hessenberg form |
g13ecc | 3 | nag_tsa_kalman_sqrt_filt_info_var One iteration step of the time-varying Kalman filter recursion using the square root information implementation |
g13edc | 3 | nag_tsa_kalman_sqrt_filt_info_invar One iteration step of the time-invariant Kalman filter recursion using the square root information implementation with in upper controller Hessenberg form |
g13ejc | 25 | nag_tsa_kalman_unscented_state_revcom Combined time and measurement update, one iteration of the Unscented Kalman Filter for a nonlinear state space model, with additive noise (reverse communication) |
g13ekc | 25 | nag_tsa_kalman_unscented_state Combined time and measurement update, one iteration of the Unscented Kalman Filter for a nonlinear state space model, with additive noise |
g13ewc | 3 | nag_tsa_trans_hessenberg_observer Unitary state-space transformation to reduce to lower or upper observer Hessenberg form |
g13exc | 3 | nag_tsa_trans_hessenberg_controller Unitary state-space transformation to reduce to lower or upper controller Hessenberg form |
g13fac | 6 | nag_tsa_uni_garch_asym1_estim Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form |
g13fbc | 6 | nag_tsa_uni_garch_asym1_forecast Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form |
g13fcc | 6 | nag_tsa_uni_garch_asym2_estim Univariate time series, parameter estimation for a GARCH process with asymmetry of the form |
g13fdc | 6 | nag_tsa_uni_garch_asym2_forecast Univariate time series, forecast function for a GARCH process with asymmetry of the form |
g13fec | 6 | nag_tsa_uni_garch_gjr_estim Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process |
g13ffc | 6 | nag_tsa_uni_garch_gjr_forecast Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process |
g13mec | 24 | nag_tsa_inhom_iema Computes the iterated exponential moving average for a univariate inhomogeneous time series |
g13mfc | 24 | nag_tsa_inhom_iema_all Computes the iterated exponential moving average for a univariate inhomogeneous time series, intermediate results are also returned |
g13mgc | 24 | nag_tsa_inhom_ma Computes the exponential moving average for a univariate inhomogeneous time series |
g13nac | 25 | nag_tsa_cp_pelt Change point detection, using the PELT algorithm |
g13nbc | 25 | nag_tsa_cp_pelt_user Change points detection using the PELT algorithm, user supplied cost function |
g13ndc | 25 | nag_tsa_cp_binary Change point detection, using binary segmentation |
g13nec | 25 | nag_tsa_cp_binary_user Change point detection, using binary segmentation, user supplied cost function |
g13xzc | 2 | nag_tsa_free Freeing function for use with g13 option setting |