NAG FL Interface
G13 (Tsa)
Time Series Analysis

G13 (Tsa) Chapter Introduction – A description of the Chapter and an overview of the algorithms available.

Routine
Mark of
Introduction

Purpose
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9 nagf_tsa_uni_diff
Univariate time series, seasonal and non-seasonal differencing
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9 nagf_tsa_uni_autocorr
Univariate time series, sample autocorrelation function
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9 nagf_tsa_uni_autocorr_part
Univariate time series, partial autocorrelations from autocorrelations
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9 nagf_tsa_uni_arima_prelim
Univariate time series, preliminary estimation, seasonal ARIMA model
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9 nagf_tsa_uni_arima_estim
Univariate time series, estimation, seasonal ARIMA model (comprehensive)
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9 nagf_tsa_uni_arima_estim_easy
Univariate time series, estimation, seasonal ARIMA model (easy-to-use)
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9 nagf_tsa_uni_arima_update
Univariate time series, update state set for forecasting
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9 nagf_tsa_uni_arima_forecast_state
Univariate time series, forecasting from state set
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10 nagf_tsa_uni_arima_forcecast
Univariate time series, state set and forecasts, from fully specified seasonal ARIMA model
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22 nagf_tsa_uni_smooth_exp
Univariate time series, exponential smoothing
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13 nagf_tsa_uni_arima_resid
Univariate time series, diagnostic checking of residuals, following g13aef or g13aff
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14 nagf_tsa_uni_means
Computes quantities needed for range-mean or standard deviation-mean plot
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25 nagf_tsa_uni_dickey_fuller_unit
Computes (augmented) Dickey–Fuller unit root test statistic
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10 nagf_tsa_multi_filter_arima
Multivariate time series, filtering (pre-whitening) by an ARIMA model
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11 nagf_tsa_multi_filter_transf
Multivariate time series, filtering by a transfer function model
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10 nagf_tsa_multi_xcorr
Multivariate time series, cross-correlations
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11 nagf_tsa_multi_transf_prelim
Multivariate time series, preliminary estimation of transfer function model
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11 nagf_tsa_multi_inputmod_estim
Multivariate time series, estimation of multi-input model
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11 nagf_tsa_multi_inputmod_update
Multivariate time series, update state set for forecasting from multi-input model
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11 nagf_tsa_multi_inputmod_forecast_state
Multivariate time series, forecasting from state set of multi-input model
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11 nagf_tsa_multi_inputmod_forecast
Multivariate time series, state set and forecasts from fully specified multi-input model
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10 nagf_tsa_uni_spectrum_lag
Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window
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10 nagf_tsa_uni_spectrum_daniell
Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
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10 nagf_tsa_multi_spectrum_lag
Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window
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10 nagf_tsa_multi_spectrum_daniell
Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window
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10 nagf_tsa_multi_spectrum_bivar
Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra
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10 nagf_tsa_multi_gain_bivar
Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra
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10 nagf_tsa_multi_noise_bivar
Multivariate time series, noise spectrum, bounds, impulse response function and its standard error
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11 nagf_tsa_multi_autocorr_part
Multivariate time series, multiple squared partial autocorrelations
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22 nagf_tsa_multi_varma_estimate
Multivariate time series, estimation of VARMA model
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15 nagf_tsa_multi_varma_forecast
Multivariate time series, forecasts and their standard errors
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15 nagf_tsa_multi_varma_update
Multivariate time series, updates forecasts and their standard errors
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15 nagf_tsa_multi_diff
Multivariate time series, differences and/or transforms
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15 nagf_tsa_multi_corrmat_cross
Multivariate time series, sample cross-correlation or cross-covariance matrices
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15 nagf_tsa_multi_corrmat_partlag
Multivariate time series, sample partial lag correlation matrices, χ2 statistics and significance levels
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16 nagf_tsa_multi_regmat_partial
Multivariate time series, partial autoregression matrices
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13 nagf_tsa_multi_varma_diag
Multivariate time series, diagnostic checking of residuals, following g13ddf
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15 nagf_tsa_uni_arma_roots
Calculates the zeros of a vector autoregressive (or moving average) operator
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17 nagf_tsa_multi_kalman_sqrt_var
Combined measurement and time update, one iteration of Kalman filter, time-varying, square root covariance filter
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17 nagf_tsa_multi_kalman_sqrt_invar
Combined measurement and time update, one iteration of Kalman filter, time-invariant, square root covariance filter
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25 nagf_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)
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25 nagf_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
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20 nagf_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 εt-1+γ2
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Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form εt-1+γ2
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20 nagf_tsa_uni_garch_asym2_estim
Univariate time series, parameter estimation for a GARCH process with asymmetry of the form εt-1+γεt-12
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Univariate time series, forecast function for a GARCH process with asymmetry of the form εt-1+γεt-12
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20 nagf_tsa_uni_garch_gjr_estim
Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
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Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
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20 nagf_tsa_uni_garch_exp_estim
Univariate time series, parameter estimation for an exponential GARCH (EGARCH) process
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Univariate time series, forecast function for an exponential GARCH (EGARCH) process
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24 nagf_tsa_inhom_iema
Computes the iterated exponential moving average for a univariate inhomogeneous time series
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24 nagf_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 nagf_tsa_inhom_ma
Computes the exponential moving average for a univariate inhomogeneous time series
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25 nagf_tsa_cp_pelt
Change point detection, using the PELT algorithm
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25 nagf_tsa_cp_pelt_user
Change points detection using the PELT algorithm, user supplied cost function
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25 nagf_tsa_cp_binary
Change point detection, using binary segmentation
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25 nagf_tsa_cp_binary_user
Change point detection, using binary segmentation, user supplied cost function