Power at Your Fingertips
The PC SCA System gives you the power to analyze time series data
using comprehensive modeling capabilities and delivers accurate forecasts that you can
depend on. Available as individual products or as a combined set of modules, the PC SCA
System is the solution to your modeling and forecasting needs. The System’s
flexibility, ease of use, and ability to grow with its user form an impressive
combination.
Basic System Features of the PC SCA System
System Requirements
Extending Core System Capabilities through SCA Applet Technology
The information on this page is specific to the 32-bit version of the PC SCA System.
You may choose among the modules listed below to address your individual forecasting and modeling needs:
| SCA Products | General Description |
|---|---|
| Educational Edition | The SCA Educational Edition is available to academic users. It provides the essential building blocks of univariate time series modeling and analysis including Box-Jenkins ARIMA, transfer function, and intervention models. It is the fundamental module on which other SCA forecasting and time series analysis products are built |
| Practitioner Edition | The Practitioner Edition builds on the Educational Edition by adding expert-system automatic time series model identification and forecasting. It also includes power packed capabilities to automatically detect and adjust for outliers during estimation which is great for data mining. The Practitioner Edition is the natural choice in driving large scale forecasting applications that rely on automation. It is an effective solution to handle repetitive modeling and forecasting tasks on a large number of time series |
| Professional (A) | The Professional Edition (A) builds on the Practitioner Edition by adding multivariate time series analysis and forecasting using vector ARIMA and simultaneous transfer function (STF) models. These advanced modeling approaches are well-suited to business, economic, industrial, and social science applications where the relationship between Y and X variables are bidirectional in nature |
| Professional (B) | The Professional Edition (B) builds on the Practitioner Edition by adding power transformation, segmented time series methods, nonlinear time series testing and identification, TAR models, threshold ARIMA, and threshold transfer function models. A new capability is also provided for time-varying paramter models and GARCH models |
| Advanced Edition | The Advanced Edition provides the full breadth of time series modeling and forecasting capabilities combining the features of Professional Editions (A) and (B) |
| GSA | The GSA component provides commonly used general statistical analysis capabilities including descriptive statistics, multiple-linear regression, ANOVA, nonparametric statistics and more. The GSA module is an integrated component of all SCA Editions |
| WorkBench | SCA WorkBench is a component of the PC SCA Statistical System providing the graphical user interface for the SCA System, spreadsheet data interface, analysis automation, and extended application environments. The SCA WorkBench component is an integrated component of all SCA Editions |
| GARCH | SCAB34S GARCH provides advanced add-on capabilities for modeling and analyzing time series data with heteroscedasticity. SCAB34S GARCH is an integrated component of the SCA Professional Edition (B) but can also be purchased separately with other SCA Editions or as a standalone software |
| SPLINES | SCAB34S SPLINES provides advanced add-on capabilities for data mining and predictive modeling using Multiple Adaptive Regression Splines (MARSPLINE), Generalized Additive Models (GAM), Alternating Conditional Expectations (ACE), Projection Pursuit Regression (PPREG), and more. SCAB34S SPLINES can be purchased as an add-on to all SCA Editions or as a standalone software |
SCA Educational Edition
Univariate Time Series Analysis
The SCA Educational Edition includes extensive forecasting and time series modeling capabilities. It is this fundamental module on which other SCA forecasting and time series products are built. The SCA Educational Edition focuses on user directed modeling capabilities, providing all the necessary tools to identify, estimate, diagnostically check, and forecast various time series models. The Educational Edition features,
- Box-Jenkins ARIMA modeling
- New identification method for seasonal ARIMA models
- Lagged (dynamic) regression
- Regression with autocorrelated errors
- Convenient transfer function modeling
- Intervention (impact) analysis
- Spectral analysis
- Exponential smoothing using Simple, Double, Holt’s, Winters’ additive, Winters’ Multiplicative, Seasonal indicator, and Harmonic smoothing methods
- Trading day adjustment
- Time series simulation
- Constrained parameter estimation
- Exact estimation algorithm
Adjustment for trading days and holiday effects
- Data that are compiled and reported on a monthly basis are often subjected
to variation due to the composition of the calendar. In addition, the occurrence of
traditional festivals or holidays, such as Easter, can be important. Variation arising
because a series varies with the days of the week is known as trading day variation.
- The SCA System provides some simple, but effective, schemes to account for both trading day variation and the variation due to the Easter holiday. The methods are based on the research and contributions of S.C. Hillmer, W.R. Bell, G.C. Tiao, L.-M. Liu, and others.
Spectral analysis
- Estimation of spectra or cross-spectra based on periodograms
- Estimation of spectra or cross-spectra based on covariances and autocovariances
- Estimation of spectra based on an ARIMA model
- Filtering using band-pass of band-reject filters
Tools for tentative model identification
- The SCA System provides the RSFILTER capability to uncomplicate
seasonal ARIMA model identification. The method was introduced in the book, Time Series
Analysis and Forecasting 2nd Edition, by L.-M. Liu (2006)
- The SCA System provides a wide array of statistical techniques
useful in the tentative identification of an ARIMA model. These include the traditional
sample autocorrelation function (ACF) and sample partial auto correlation function (PACF).
- In addition, two methods developed by G.C. Tiao and R.S. Tsay
are included. These are the extended sample autocorrelation function (EACF) and the
smallest canonical correlation (SCAN) methods. The EACF and SCAN methods have been found
to be very effective in the identification of mixed ARIMA models.
- The SCA System provides a choice of identification techniques for transfer function modeling. One is based on the traditional Box-Jenkins cross correlation approach. The other employs a linear transfer function method by L.-M. Liu and D.M. Hanssens. This newer method has also shown itself to be very effective for the identification of ARIMA models in the presence of trading day and holiday effects.
Unlimited modeling capability
- Any number of different models can be specified and retained during an SCA session. All models can reside in memory simultaneously. Additionally, the System has no special restrictions on the number of parameters, the number of variables, or the number of observations in a model. The only restriction is in the overall size of the SCA memory space allocated by the user.
Model estimation-System accuracy
- The SCA System provides both a conditional and an exact maximum
likelihood algorithm for univariate ARIMA model estimation. The exact likelihood algorithm
is especially important in the estimation of seasonal ARIMA models.
- A modified algorithm developed by L.-M. Liu is utilized in the
estimation of transfer function models. This algorithm avoids a major flaw in transfer
function estimation of other estimation algorithms.
- The SCA System has respected reputation for accuracy. It is cited frequently in statistical journals and is used for critical analyzes for time series research at corporations, governments, universities, and research organizations worldwide.
Outlier detection
- A time series is often subjected to influences of external events.
If these events, and their related effects, are either unknown or not accounted for,
inappropriate models or biased parameter estimates can result.
- The SCA System provides capabilities for the detection and classification of various types of outliers (spurious observations) in a time series. These methods are based on the original work of S. C. Hillmer, W.R. Bell, G.C. Tiao, I. Chang, and C. Chen.
- The SCA System provides extended capabilities to jointly estimate outlier effects and model parameters in an automated fashion. Please see the Practitioner Edition below for more details.
SCA Practitioner Edition
Automatic time series modeling using an expert system approach
The SCA Practitioner Edition employs an intelligent algorithm for automatic time series modeling. It is very easy to use, and is an asset to novices and experts alike, offering a quick and effective solution to handle repetitive modeling tasks on large amounts of data. The SCA Practitioner Edition features,
- Automatic identification of seasonal and non-seasonal ARIMA models
- Automatic transfer function modeling and intervention (impact) analysis
- Automatic vector ARMA modeling (requires Professional Edition (A) )
- Reliable and accurate results relieving mundane modeling chores
- Manual override of models allowing complete flexibility
- Includes the complete capabilities of the SCA Educational Edition
The Practitioner Edition also provides cutting edge capabilities
to conveniently handle contaminated or interrupted time series that may otherwise distort the underlying
model structure, cause bias in parameter estimates, and lead to a deterioration in forecast
performance. These capabilities address,
Automatic outlier detection and adjustment
capabilities that allow for the joint estimation and of outlier effects and model parameters
based on the published works of C. Chen and L.-M. Liu
SCA Professional Edition (A)
Multivariate Time Series Modeling and Analysis The SCA Professional Edition (A) provides the complete capabilities of the Practitioner Edition and adds state-of-the-art capabilities for modeling and forecasting multivariate time series data using vector ARMA models and simultaneous transfer function (STF) models. These modeling approaches are well-suited to business, econometric, industrial and social science time series data.
Vector ARMA
The Vector ARMA approach to model multiple time series data was developed by G.C. Tiao and G.E.P. Box. It is an extremely valuable modeling method to analyze and forecast dynamic variable systems in terms of leading, lagging, and feedback relationships. The PC-MTS module features,
- Comprehensive model identification techniques
- Conditional and exact maximum likelihood estimation
- Principal component analyses
- Canonical analysis
Tentative model identification
- The multivariate extensions to the univariate sample
autocorrelation function (PACF) are provided. They are sample cross-correlation matrices
(CCM) and stepwise autoregressive fits (STEPAR).
- Additionally, the multivariate extensions of the univariate extended sample autocorrelation function (EACF) and the smallest canonical correlation (SCAN) are also provided. These extensions, developed by G.C. Tiao and R.S. Tsay, are very effective in the identification of mixed ARMA models and in discovering underlying relationships between series.
Model estimation
- Parameters of a vector model can be estimated using either a conditional or exact maximum likelihood algorithm. In addition, ARMA parameters may be held to fixed values during the estimation process (such as zero), or can be constrained to be equal to other parameters.
Simultaneous Transfer Function (STF) Models
STF models allow for a system of transfer function models to be estimated and forecasted jointly. STF models may be
specified in reduced form, similar to vector ARMA models, or in structural form allowing for contemporaneous relationships
to exist between variables. Furthermore, STF models may also include model components to handle
interventions as well as trading day and moving holiday effects that often occur in business and economic applications.
Econometric Modeling Using STF Models
- Traditional
econometric modeling and time series analysis are blended using simultaneous transfer function
methodologies. Using this approach, many potential problems found in classical
econometric analysis are avoided.
- One major problem of traditional econometric models is the
assumption that disturbance (error) components are serially independent. Such an
assumption can cause erroneous results when econometric models are applied to time series
data. Within the STF modeling framework, such erroneous results may be avoided by adding an ARIMA
component to each individual equation that violates the assumption of serially independent
error.
- An added feature is that the coefficient of each input variable of an equation can be represented in either a linear or a rational form. This class of econometric models is also referred to a simultaneous transfer function (STF) models, or as rational distributed lag structured form (RSF) models.
Encompassing conventional modeling approaches:
Expressing models in a STF forms results in encompassing the following conventional econometric modeling features:
- Regression with first of second order serial correlation (Cochrane-Orcutt and Hildreth-Lu methods)
- Generalized Least Squares (GLS) with first or second order serial correlation
- Lagged regression models with AR, MA, or ARMA noise
- Geometric lag models with ARMA noise
- Rational distributed lag models with ARMA noise
- Ridge regression
- Seemingly unrelated regression
- Linear Structural form and reduced form models
- Rational structural form and reduced form models
Model specification and estimation
- Simultaneous equation systems are easily specified. Each
individual equation is specified as if a univariate time series model. Bringing together
individual equations, and any identity equation, is then directly done.
- Initial parameter values can be created by the System, be the results of previous univariate modeling or be specified directly by the user. Parameters of a simultaneous system are estimated using the full information maximum likelihood (FIML) method. Estimated values can be retained for future use.
Seasonal adjustment procedures
- An ARIMA model-based procedure developed by S.C. Hillmer and G.C. Tiao
The GSA component provides capabilities for general statistical analysis. This module is an integrated component of all SCA Editions. The GSA component provides,
- Descriptive statistics and correlation
- Plots, histograms, and two-way tables
- Multiple regression analysis
- One-way to n-way ANOVA
- Analysis of covariance
- Two-sample tests of significance
- Cross tabulation
- Nonparametric statistics
- Distribution and model simulation
The SCAGRAF module is a convenient and easy to use capability providing high resolution color graphics for applications in time series analysis, quality control, and general data analysis. SCAGRAF is an integrated component of the PC SCA System for Windows. SCAGRAF features,
- Single and multiple time series plots
- Single and multiple scatter plots
- Autocorrelation and partial autocorrelation plots
- Forecast plots with confidence bands
- Outlier plots with AO, IO, LS and TC designation
- Temporal aggregation plots
- Scatter plots with regression line
- Box-Cox transformation plots
- Contour plots
- S, x, p, np, c and u quality control charts
- Provides a file interface with SCA WorkBench and SCA Applets
Basic System Features under MS Windows (All products)
- Compatible with MS Windows Vista, XP, and NT
- High resolution color graphics
- Convenient and powerful command interface
- Extensive analytic functions and matrix operations
- Programmable command language
- Task automation through macro procedures
- Launch from spreadsheet/database applications as a statistical forecasting engine
- Execute external programs through SCA Applet technology
- Data generation, editing, sorting, and ranking
- Example-driven and easy to use documentation
- Expert statistical and user support services
- Integrates with SCA WorkBench to manage analyses of large number of series or datasets
- IBM PC or compatible (Pentium or above)
- MS Windows Vista, XP, or NT
- Minimum RAM is 1GB (Vista), 256MB (XP)