SAS/Ets User's Guide 161. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 1592 F Chapter 23 The SIMILARITY Procedure All results of the similarity analysis can be stored in output data sets printed or graphed using the Output Delivery System ODS . The SIMILARITY procedure can process large amounts of time-stamped transactional data time series or sequential data. Therefore the analysis results are useful for large-scale time series analysis analogous time series forecasting new product forecasting or time series temporal data mining. The SAS ETS EXPAND procedure can be used for frequency conversion and transformations of time series. The TIMESERIES procedure can be used for large-scale time series analysis. The SAS STAT DISTANCE procedure can be used to compute various measures of distance dissimilarity or similarity between observations rows of a SAS data set. Getting Started SIMILARITY Procedure This section outlines the use of the SIMILARITY procedure and gives a cursory description of some of the analysis techniques that can be performed on time-stamped transactional data time series or sequentially ordered numeric data. Given an input data set that contains numerous transaction variables recorded over time at no specific frequency the SIMILARITY procedure can form equally spaced input and target time series as follows PROC SIMILARITY DATA input-data-set OUT output-data-set OUTSUM summary-data-set ID time-ID-variable INTERVAL frequency ACCUMULATE statistic INPUT input-time-stamp-variables TARGET target-time-stamp-variables RUN The SIMILARITY procedure forms time series from the input time-stamped transactional data. It can provide results in output data sets or in other output formats using the Output Delivery System ODS . The examples in this section are more fully illustrated in the section Examples SIMILARITY Procedure on page 1633. Time-stamped transactional data are often recorded at no fixed interval. Analysts often want to use time series analysis techniques that require fixed-time intervals. Therefore the transactional data .
