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