Time Series Analysis Toolbox for OMatrix by Harmonic Software
The STSA (Statistical Time Series Analysis) Toolbox is an extensive collection of OMatrix functions for performing time series related analysis and visualization. The STSA toolbox provides extensive capabilities for ARMA and ARFIMA, Bayesian, nonlinear and spectral analysis related models.
The STSA (Statistical Time Series Analysis) Toolbox is an extensive
collection of OMatrix functions for performing time
series related analysis and visualization.
The STSA toolbox provides
extensive capabilities for ARMA and ARFIMA, Bayesian,
nonlinear and spectral analysis related models.
The STSA toolbox aids in the rapid solution of many time series problems,
some of which cannot be easily dealt with using a canned program
or are not directly available in most analysis software packages.
For example, the NONLIN directory provides functions for
model selection, estimation and forecasting for the class of
functional coefficient autoregressive models: this is a
stateoftheart class of powerful and flexible
nonparametric models that can be used in forecasting nonlinear time series.
And, in the SPECTRAL directory the user can find functions for simulating,
estimating and forecasting longmemory time series, a class of time series
that is encountered in such diverse fields as hydrology and finance.
The BAYES directory includes functions for Bayesian modeling and
forecasting of time series that are not typically available in a
commercial statistical package.
The Bayesian techniques of this directory offer a greater degree of
flexibility than traditional linear models and can handle a large
number of forecasting tasks.
The ARMA directory provides over 40 functions for
analyzing uni and multidimensional time series via the class of ARMAtype models.
A few areas of application of the STSA toolbox include:
 Forecasting in economics and finance.
 Sales forecasting and inventory control.
 Industrial forecasting and process control.
 Specification of multidimensional models (transfer functions and VARs) and nonlinear models in fields like astronomy, hydrology and operations research.
The STSA toolbox can be used by practitioners in a variety of disciplines.
It can also be used for classroom instruction, as it allows the instructor to
concentrate on the actual application of time series concepts and not on programming.
The STSA toolbox offers a complete solution for doing time
series analysis: from simulation, to estimation, to residual analysis,
to forecasting, complete with many auxiliary statistical functions
for descriptive statistics, statistical plots, trend forecasting,
time series smoothing and others. The toolbox comes with extensive
documentation and numerous examples.
STSA Functional Categories Synopsis:
 ARMA Analysis
 Univariate Analysis Functions
 Create a matrix of sequential lags of a time series
 Estimate and plot the autocovariance function (ACVF), autocorrelation function (ACF)
and partial autocorrelation function (PACF)
 Compute the roots of the characteristic polynomials of an estimate ARIMA/ARFIMA model
 Compute the coefficients in the infinite AR or MA representation of an ARIMA/ARFIMA model
 Forecast (predict) future observations of the time series using an estimated model
 Filter a time series using an estimated model
 Validate a model using a variety of residual diagnostics
 Multivariate Analysis Functions
 Estimate the crosscorrelation function (CCF) between pairs of time series
 Estimate by conditional, nonlinear least squares parameters of a TF model
 Select the order of a VAR model using two model selection criteria
 Estimate by conditional linear least squares the parameters of a VAR model
 Forecast (predict) future observations of the time series using an estimated model
 Validate a model using a variety of residual diagnostics applied to each individual residual series
 Bayesian Analysis
 Simulate a first order polynomial DLM
 Initialize the Bayesian recursions using arbitrary priors or reference priors
 Automatically check the observability of a DLM
 Automatically construct the system and observation matrices for a
number of popular DLMs, including DLMs for seasonal time series
 Fit a variety of DLM models to a time series
 Perform out of sample forecasting using the fitted DLM
 Compute interval forecasts and standardized forecast errors
 Evaluate the forecasting performance of a DLM using the mean squared and
mean absolute values of the forecast errors
 Nonlinear Analysis
 Select the order, delay and threshold values for a TAR model
 Estimate by conditional, linear least squares the parameters of a selected TAR model
 Select the order, delay and bandwidth values for a FCAR model
 Estimate by conditional, linear weighted least squares (WLS) the
functional parameters of a selected FCAR model
 Compute the sequence of onestep
ahead forecasts from an estimated FCAR model
 Spectral
 Estimate the Fourier transform of a time series either using the
discrete Fourier or the fast Fourier method
 Estimate the periodogram of a time series from the
Fourier transform
 Smooth the periodogram of a time series to obtain its spectrum
using a sine taper function
 Estimate the periodogram of a time series using an AR model approximation
 Plot the estimated spectrum of a time series
System Requirements
 OMatrix 5.6 or greater
 Windows NT, 2000, or XP
 1 MB Available Disk Space
