NAG Toolbox

G13 – time series analysis

• 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, χ² 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

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