Time Series Analysis and Forecasting
GENERAL METHODLOGY
TIME SERIES TRANSFORMATION
| ROUTINE | DESCRIPTION |
|---|---|
| BCTR | Performs a forward or an inverse Box-Cox (power) transformation. |
| DIFF | Differences a time series. |
| ESTIMATE_MISSING | Estimates missing values in a time series. |
| SEASONAL_FIT | Determines an optimal differencing for seasonal adjustments of a time series. |
TIME SERIES TRANSFORMATION
| ROUTINE | DESCRIPTION |
|---|---|
| ACF | Computes the sample autocorrelation function of a stationary time series. |
| PACF | Computes the sample partial autocorrelation function of a stationary time series. |
| CCF | Computes the sample cross-correlation function of two stationary time series |
| MCCF | Computes the multichannel cross-correlation function of two mutually stationary multichannel time series. |
TIME DOMAIN METHODOLOGY
NONSEASONAL TIME SERIES MODEL ESTIMATION
| ROUTINE | DESCRIPTION |
|---|---|
| ARMME | Computes method of moments estimates of the autoregressive parameters of an ARMA model. |
| MAMME | Computes method of moments estimates of the moving average parameters of an ARMA model. |
| NSPE | Computes preliminary estimates of the autoregressive and moving average parameters of an ARMA model. |
| NSLSE | Computes least-squares estimates of parameters for a nonseasonal ARMA model. |
| MAX_ARMA | Exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive, moving average) time series model. |
| REG_ARIMA | Fits a univariate, non-seasonal ARIMA time series model with the inclusion of one or more regression variables. |
| GARCH | Computes estimates of the parameters of a GARCH(p,q) model. |
| GARCH | Computes the Wiener forecast operator for a stationary stochastic process. |
| NSBJF | Computes Box-Jenkins forecasts and their associated probability limits for a nonseasonal ARMA model. |
TRANSFER FUNCTION MODEL
| ROUTINE | DESCRIPTION |
|---|---|
| IRNSE | Computes estimates of the impulse response weights and noise series of a univariate transfer function model. |
| TFPE | Computes preliminary estimates of parameters for a univariate transfer function model. |
MULTICHANNEL TIME SERIES
| ROUTINE | DESCRIPTION |
|---|---|
| MLSE | Computes least-squares estimates of a linear regression model for a multichannel time series with a specified base channel. |
| MWFE | Computes least-squares estimates of the multichannel Wiener filter coefficients for two mutually stationary multichannel time series. |
| KALMN | Performs Kalman filtering and evaluates the likelihood function for the stat-espace model. |
AUTOMATIC MODEL SELECTION FITTING
| ROUTINE | DESCRIPTION |
|---|---|
| AUTO_UNI_AR | Automatic selection and fitting of a univariate autoregressive time series model. |
| TS_OUTLIER_IDENTIFICATION | Detects and determines outliers and simultaneously estimates the model parameters in a time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model. |
| TS_OUTLIER_FORECAST | Computes forecasts, associated probability limits and Ψ weights for an outlier contaminated time series. |
| AUTO_ARIMA | Automatically identifies time series outliers, determines parameters of a multiplicative seasonal ARIMA (p,0, q) x (0, d,0)s model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series. |
| AUTO_FPE_UNI_AR | Automatic selection and fitting of a univariate autoregressive time series model using Akaike's Final Prediction Error (FPE) criteria. |
| AUTO_PARM | Estimates structural breaks in non-stationary univariate time series. |
| AUTO_MUL_AR | Automatic selection and fitting of a multivariate autoregressive time series model. |
| AUTO_FPE_MUL_AR | Automatic selection and fitting of a multivariate autoregressive time series model using Akaike's Multivariate Final Prediction Error (MFPE) criteria. |
BAYESIAN TIME SERIES ESTIMATION
| ROUTINE | DESCRIPTION |
|---|---|
| BAY_SEA | Bayesian seasonal adjustment modeling. The model allows for a decomposition of a time series into trend, seasonal, and an error component. |
CONTROLLER DESIGN
| ROUTINE | DESCRIPTION |
|---|---|
| OPT_DES | Optimal controller design which allows for multiple channels for both the controlled and manipulated variables. |
DIAGNOSTICS
| ROUTINE | DESCRIPTION |
|---|---|
| LOFCF | Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function. |
CONTROLLER DESIGN
| ROUTINE | DESCRIPTION |
|---|---|
| OPT_DES | Optimal controller design which allows for multiple channels for both the controlled and manipulated variables. |
FREQUENCY DOMAIN METHODOLOGY
SMOOTHING FUNCTIONS
| ROUTINE | DESCRIPTION |
|---|---|
| DIRIC | Computes the Dirichlet kernel. |
| FEJER | Computes the Fejér kernel. |
SPECTRAL DENSITY ESTIMATION
| ROUTINE | DESCRIPTION |
|---|---|
| ARMA_SPEC | Calculates the rational power spectrum for an ARMA model. |
| PFFT | Calculates the rational power spectrum for an ARMA model. |
| SSWD | Estimates the nonnormalized spectral density of a stationary time series using a spectral window given the time series data. |
| SSWP | Estimates the nonnormalized spectral density of a stationary time series using a spectral window given the periodogram. |
| SWED | Estimates the nonnormalized spectral density of a stationary time series based on specified periodogram weights given the time series data. |
| SWEP | Estimates the nonnormalized spectral density of a stationary time series based on specified periodogram weights given the periodogram. |
CROSS-SPECTRAL DENSITY ESTIMATION
| ROUTINE | DESCRIPTION |
|---|---|
| CPFFT | Computes the cross periodogram of two stationary time series using a fast Fourier transform. |
| CSSWD | Estimates the nonnormalized cross-spectral density of two stationary time series using a spectral window given the time series data. |
| CSSWP | Estimates the nonnormalized cross-spectral density of two stationary time series using a spectral window given the spectral densities and cross periodogram |
| CSWED | Estimates the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the time series data. |
| CSWEP | Estimates the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the spectral densities and cross periodogram. |
