NAG Toolbox |

- G13 Introduction
- g13aa – Univariate time series, seasonal and non-seasonal differencing
- nag_tsa_uni_diff
- g13ab – Univariate time series, sample autocorrelation function
- nag_tsa_uni_autocorr
- g13ac – Univariate time series, partial autocorrelations from autocorrelations
- nag_tsa_uni_autocorr_part
- g13ad – Univariate time series, preliminary estimation, seasonal ARIMA model
- nag_tsa_uni_arima_prelim
- g13ae – Univariate time series, estimation, seasonal ARIMA model (comprehensive)
- nag_tsa_uni_arima_estim
- g13af – Univariate time series, estimation, seasonal ARIMA model (easy-to-use)
- nag_tsa_uni_arima_estim_easy
- g13ag – Univariate time series, update state set for forecasting
- nag_tsa_uni_arima_update
- g13ah – Univariate time series, forecasting from state set
- nag_tsa_uni_arima_forecast_state
- g13aj – Univariate time series, state set and forecasts, from fully specified seasonal ARIMA model
- nag_tsa_uni_arima_forcecast
- g13am – Univariate time series, exponential smoothing
- nag_tsa_uni_smooth_exp
- g13as – Univariate time series, diagnostic checking of residuals, following g13ae or g13af
- nag_tsa_uni_arima_resid
- g13au – Computes quantities needed for range-mean or standard deviation-mean plot
- nag_tsa_uni_means
- g13aw – Computes (augmented) Dickey–Fuller unit root test statistic
- nag_tsa_uni_dickey_fuller_unit
- g13ba – Multivariate time series, filtering (pre-whitening) by an ARIMA model
- nag_tsa_multi_filter_arima
- g13bb – Multivariate time series, filtering by a transfer function model
- nag_tsa_multi_filter_transf
- g13bc – Multivariate time series, cross-correlations
- nag_tsa_multi_xcorr
- g13bd – Multivariate time series, preliminary estimation of transfer function model
- nag_tsa_multi_transf_prelim
- g13be – Multivariate time series, estimation of multi-input model
- nag_tsa_multi_inputmod_estim
- g13bg – Multivariate time series, update state set for forecasting from multi-input model
- nag_tsa_multi_inputmod_update
- g13bh – Multivariate time series, forecasting from state set of multi-input model
- nag_tsa_multi_inputmod_forecast_state
- g13bj – Multivariate time series, state set and forecasts from fully specified multi-input model
- nag_tsa_multi_inputmod_forecast
- g13ca – Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window
- nag_tsa_uni_spectrum_lag
- g13cb – Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
- nag_tsa_uni_spectrum_daniell
- g13cc – Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window
- nag_tsa_multi_spectrum_lag
- g13cd – Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window
- nag_tsa_multi_spectrum_daniell
- g13ce – Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra
- nag_tsa_multi_spectrum_bivar
- g13cf – Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra
- nag_tsa_multi_gain_bivar
- g13cg – Multivariate time series, noise spectrum, bounds, impulse response function and its standard error
- nag_tsa_multi_noise_bivar
- g13db – Multivariate time series, multiple squared partial autocorrelations
- nag_tsa_multi_autocorr_part
- g13dd – Multivariate time series, estimation of VARMA model
- nag_tsa_multi_varma_estimate
- g13dj – Multivariate time series, forecasts and their standard errors
- nag_tsa_multi_varma_forecast
- g13dk – Multivariate time series, updates forecasts and their standard errors
- nag_tsa_multi_varma_update
- g13dl – Multivariate time series, differences and/or transforms
- nag_tsa_multi_diff
- g13dm – Multivariate time series, sample cross-correlation or cross-covariance matrices
- nag_tsa_multi_corrmat_cross
- g13dn – Multivariate time series, sample partial lag correlation matrices, χ
^{2}statistics and significance levels - nag_tsa_multi_corrmat_partlag
- g13dp – Multivariate time series, partial autoregression matrices
- nag_tsa_multi_regmat_partial
- g13ds – Multivariate time series, diagnostic checking of residuals, following g13dd
- nag_tsa_multi_varma_diag
- g13dx – Calculates the zeros of a vector autoregressive (or moving average) operator
- nag_tsa_uni_arma_roots
- g13ea – Combined measurement and time update, one iteration of Kalman filter, time-varying, square root covariance filter
- nag_tsa_multi_kalman_sqrt_var
- g13eb – Combined measurement and time update, one iteration of Kalman filter, time-invariant, square root covariance filter
- nag_tsa_multi_kalman_sqrt_invar
- g13ej – Combined time and measurement update, one iteration of the Unscented Kalman Filter for a nonlinear state space model, with additive noise (reverse communication)
- nag_tsa_kalman_unscented_state_revcom
- g13ek – Combined time and measurement update, one iteration of the Unscented Kalman Filter for a nonlinear state space model, with additive noise
- nag_tsa_kalman_unscented_state
- g13fa – Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the
form (ε
_{t-1}+γ)^{2} - nag_tsa_uni_garch_asym1_estim
- g13fb – Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form
(ε
_{t-1}+γ)^{2} - nag_tsa_uni_garch_asym1_forecast
- g13fc – Univariate time series, parameter estimation for a GARCH process with asymmetry of the form (|ε
_{t-1}|+γε_{t-1})^{2} - nag_tsa_uni_garch_asym2_estim
- g13fd – Univariate time series, forecast function for a GARCH process with asymmetry of the form (|ε
_{t-1}|+γε_{t-1})^{2} - nag_tsa_uni_garch_asym2_forecast
- g13fe – Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
- nag_tsa_uni_garch_gjr_estim
- g13ff – Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
- nag_tsa_uni_garch_gjr_forecast
- g13fg – Univariate time series, parameter estimation for an exponential GARCH (EGARCH) process
- nag_tsa_uni_garch_exp_estim
- g13fh – Univariate time series, forecast function for an exponential GARCH (EGARCH) process
- nag_tsa_uni_garch_exp_forecast
- g13me – Computes the iterated exponential moving average for a univariate inhomogeneous time series
- nag_tsa_inhom_iema
- g13mf – Computes the iterated exponential moving average for a univariate inhomogeneous time series, intermediate results are also returned
- nag_tsa_inhom_iema_all
- g13mg – Computes the exponential moving average for a univariate inhomogeneous time series
- nag_tsa_inhom_ma
- g13na – Change point detection, using the PELT algorithm
- nag_tsa_cp_pelt
- g13nb – Change points detection using the PELT algorithm, user supplied cost function
- nag_tsa_cp_pelt_user
- g13nd – Change point detection, using binary segmentation
- nag_tsa_cp_binary
- g13ne – Change point detection, using binary segmentation, user supplied cost function
- nag_tsa_cp_binary_user

G12 |
H |