TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 167

SAS/Ets User's Guide 167. 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 | 1652 F Chapter 23 The SIMILARITY Procedure Example Sliding Similarity Analysis This example illustrates how to use sliding similarity analysis to compare two time sequences. The data set contains two similar time series variables ELECTRIC and MASONRY which represent employment over time. The following statements create an example data set that contains two time series of differing lengths where the variable MASONRY has the first 12 and last 7 observations set to missing to simulate the lack of data associated with the target series data workers set if 01JAN1978 D date 01JAN1982 D then masonry masonry else masonry . run The goal of sliding similarity measures analysis is find the slide index that corresponds to the most similar subsequence of the input series when compared to the target sequence. The following statements perform sliding similarity analysis on the example data set proc similarity data workers out _NULL_ print slides summary id date interval month input electric target masonry slide index measure msqrdev expand localabs 3 globalabs 3 compress localabs 3 globalabs 3 run The DATA WORKERS option specifies that the input data set is to be used in the analysis. The OUT _NULL_ option specifies that no output time series data set is to be created. The PRINT SLIDES SUMMARY option specifies that the ODS tables related to the sliding similarity measures and their summary be produced. The INPUT statement specifies that the input variable is ELECTRIC. The TARGET statement specifies that the target variable is MASONRY and that the similarity measure be computed using mean squared deviation MEASURE MSQRDEV . The SLIDE INDEX option specifies observation index sliding. The COMPRESS LOCALABS 3 GLOBALABS 3 option limits local and global absolute compression to 3. The EXPAND LOCALABS 3 GLOBALABS 3 option limits local and global absolute expansion to 3. Example Sliding Similarity Analysis F 1653 Output Summary

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