g13 Chapter Introduction – a description of the Chapter and an overview of the algorithms available
| Function Name |
Mark of Introduction |
Purpose |
| g13aac
Example Text Example Data |
7 | nag_tsa_diff Univariate time series, seasonal and non-seasonal differencing |
| g13abc
Example Text Example Data |
2 | nag_tsa_auto_corr Sample autocorrelation function |
| g13acc
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2 | nag_tsa_auto_corr_part Partial autocorrelation function |
| g13amc
Example Text Example Data |
9 | nag_tsa_exp_smooth Univariate time series, exponential smoothing |
| g13asc
Example Text Example Data |
6 | nag_tsa_resid_corr Univariate time series, diagnostic checking of residuals, following nag_tsa_multi_inp_model_estim (g13bec) |
| g13auc
Example Text Example Data Example Plot |
7 | nag_tsa_mean_range Computes quantities needed for range-mean or standard deviation-mean plot |
| g13awc
Example Text Example Data |
25 | nag_tsa_dickey_fuller_unit Computes (augmented) Dickey–Fuller unit root test statistic |
| g13bac
Example Text Example Data |
7 | nag_tsa_arma_filter Multivariate time series, filtering (pre-whitening) by an ARIMA model |
| g13bbc
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7 | nag_tsa_transf_filter Multivariate time series, filtering by a transfer function model |
| g13bcc
Example Text Example Data |
7 | nag_tsa_cross_corr Multivariate time series, cross-correlations |
| g13bdc
Example Text Example Data |
7 | nag_tsa_transf_prelim_fit Multivariate time series, preliminary estimation of transfer function model |
| g13bec
Example Text Example Data |
2 | nag_tsa_multi_inp_model_estim Estimation for time series models |
| g13bgc
Example Text Example Data |
8 | nag_tsa_multi_inp_update Multivariate time series, update state set for forecasting from multi-input model |
| g13bjc
Example Text Example Data |
2 | nag_tsa_multi_inp_model_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
Example Text Example Data |
7 | nag_tsa_spectrum_univar_cov Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| g13cbc
Example Text Example Data |
4 | nag_tsa_spectrum_univar Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| g13ccc
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7 | nag_tsa_spectrum_bivar_cov Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| g13cdc
Example Text Example Data |
4 | nag_tsa_spectrum_bivar Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| g13cec
Example Text Example Data |
4 | nag_tsa_cross_spectrum_bivar Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra |
| g13cfc
Example Text Example Data |
4 | nag_tsa_gain_phase_bivar Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra |
| g13cgc
Example Text Example Data |
4 | nag_tsa_noise_spectrum_bivar Multivariate time series, noise spectrum, bounds, impulse response function and its standard error |
| g13dbc
Example Text Example Data |
7 | nag_tsa_multi_auto_corr_part Multivariate time series, multiple squared partial autocorrelations |
| g13ddc
Example Text Example Data |
8 | nag_tsa_varma_estimate Multivariate time series, estimation of VARMA model |
| g13djc
Example Text Example Data |
8 | nag_tsa_varma_forecast Multivariate time series, forecasts and their standard errors |
| g13dkc
Example Text Example Data |
8 | nag_tsa_varma_update Multivariate time series, updates forecasts and their standard errors |
| g13dlc
Example Text Example Data |
7 | nag_tsa_multi_diff Multivariate time series, differences and/or transforms |
| g13dmc
Example Text Example Data |
7 | nag_tsa_multi_cross_corr Multivariate time series, sample cross-correlation or cross-covariance matrices |
| g13dnc
Example Text Example Data |
7 | nag_tsa_multi_part_lag_corr Multivariate time series, sample partial lag correlation matrices, statistics and significance levels |
| g13dpc
Example Text Example Data |
7 | nag_tsa_multi_part_regsn Multivariate time series, partial autoregression matrices |
| g13dsc
Example Text Example Data |
8 | nag_tsa_varma_diagnostic Multivariate time series, diagnostic checking of residuals, following nag_tsa_varma_estimate (g13ddc) |
| g13dxc
Example Text Example Data |
7 | nag_tsa_arma_roots Calculates the zeros of a vector autoregressive (or moving average) operator |
| g13eac
Example Text |
3 | nag_kalman_sqrt_filt_cov_var One iteration step of the time-varying Kalman filter recursion using the square root covariance implementation |
| g13ebc
Example Text Example Data |
3 | nag_kalman_sqrt_filt_cov_invar One iteration step of the time-invariant Kalman filter recursion using the square root covariance implementation with in lower observer Hessenberg form |
| g13ecc
Example Text Example Data |
3 | nag_kalman_sqrt_filt_info_var One iteration step of the time-varying Kalman filter recursion using the square root information implementation |
| g13edc
Example Text Example Data |
3 | nag_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
Example Text Example Data Example Plot |
25 | nag_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
Example Text Example Data Example Plot |
25 | nag_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
Example Text Example Data |
3 | nag_trans_hessenberg_observer Unitary state-space transformation to reduce to lower or upper observer Hessenberg form |
| g13exc
Example Text Example Data |
3 | nag_trans_hessenberg_controller Unitary state-space transformation to reduce to lower or upper controller Hessenberg form |
| g13fac
Example Text |
6 | nag_estimate_agarchI Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form |
| g13fbc | 6 | nag_forecast_agarchI Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form |
| g13fcc
Example Text |
6 | nag_estimate_agarchII Univariate time series, parameter estimation for a GARCH process with asymmetry of the form |
| g13fdc | 6 | nag_forecast_agarchII Univariate time series, forecast function for a GARCH process with asymmetry of the form |
| g13fec
Example Text |
6 | nag_estimate_garchGJR Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process |
| g13ffc | 6 | nag_forecast_garchGJR Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process |
| g13mec
Example Text Example Data Example Plot |
24 | nag_tsa_inhom_iema Computes the iterated exponential moving average for a univariate inhomogeneous time series |
| g13mfc
Example Text Example Data |
24 | nag_tsa_inhom_iema_all Computes the iterated exponential moving average for a univariate inhomogeneous time series, intermediate results are also returned |
| g13mgc
Example Text Example Data |
24 | nag_tsa_inhom_ma Computes the exponential moving average for a univariate inhomogeneous time series |
| g13nac
Example Text Example Data Example Plot |
25 | nag_tsa_cp_pelt Change point detection, using the PELT algorithm |
| g13nbc
Example Text Example Data Example Plot |
25 | nag_tsa_cp_pelt_user Change points detection using the PELT algorithm, user supplied cost function |
| g13ndc
Example Text Example Data Example Plot |
25 | nag_tsa_cp_binary Change point detection, using binary segmentation |
| g13nec
Example Text Example Data Example Plot |
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 |