TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 187

SAS/Ets User's Guide 187. 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 | 1852 F Chapter 29 The TIMESERIES Procedure proc timeseries data transactions out timeseries by customer id date interval day accumulate total var withdrawals deposits run The OUT TIMESERIES option specifies that the resulting time series data for each customer is to be stored in the data set . The INTERVAL DAY option specifies that the transactions are to be accumulated on a daily basis. The ACCUMULATE TOTAL option specifies that the sum of the transactions is to be calculated. After the transactional data is accumulated into a time series format many of the procedures provided with SAS ETS software can be used to analyze the resulting time series data. For example the ARIMA procedure can be used to model and forecast each customer s withdrawal data by using an ARIMA 0 1 1 0 1 1 s model where the number of seasons is s 7 days in a week using the following statements proc arima data timeseries identify var withdrawals 1 7 noprint estimate q 1 7 outest estimates noprint forecast id date interval day out forecasts quit The OUTEST ESTIMATES data set contains the parameter estimates of the model specified. The OUT FORECASTS data set contains forecasts based on the model specified. See the SAS ETS ARIMA procedure for more detail. A single set of transactions can be very large and must be summarized in order to analyze them effectively. Analysts often want to examine transactional data for trends and seasonal variation. To analyze transactional data for trends and seasonality statistics must be computed for each time period and season of concern. For each observation the time period and season must be determined and the data must be analyzed based on this determination. The following statements illustrate how to use the TIMESERIES procedure to perform trend and seasonal analysis of time-stamped transactional data. proc timeseries data transactions out out outseason season outtrend trend by customer id date interval day accumulate total var withdrawals .

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