Overview: TIMESERIES Procedure

The TIMESERIES procedure analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. The procedure can perform trend and seasonal analysis on the transactions. After the transactional data are accumulated, time domain and frequency domain analysis can be performed on the accumulated time series.

For seasonal analysis of the transaction data, various statistics can be computed for each season. For trend analysis of the transaction data, various statistics can be computed for each time period. The analysis is similar to applying the MEANS procedure of Base SAS software to each season or time period of concern.

After the transactional data are accumulated to form a time series and any missing values are interpreted, the accumulated time series can be functionally transformed using log, square root, logistic, or Box-Cox transformations. The time series can be further transformed using simple and/or seasonal differencing. After functional and difference transformations have been applied, the accumulated and transformed time series can be stored in an output data set. This working time series can then be analyzed further using various time series analysis techniques provided by this procedure or other SAS/ETS procedures.

Time series analyses performed by the TIMESERIES procedure include:

• descriptive (global) statistics

• seasonal decomposition/adjustment analysis

• correlation analysis

• cross-correlation analysis

All results of the transactional or time series analysis can be stored in output data sets or printed using the Output Delivery System (ODS).

The TIMESERIES procedure can process large amounts of time-stamped transactional data. Therefore, the analysis results are useful for large-scale time series analysis or (temporal) data mining. All of the results can be stored in output data sets in either a time series format (default) or in a coordinate format (transposed). The time series format is useful for preparing the data for subsequent analysis with other SAS/ETS procedures. For example, the working time series can be further analyzed, modeled, and forecast with other SAS/ETS procedures. The coordinate format is useful when using this procedure with SAS/STAT procedures or SAS Enterprise Miner. For example, clustering time-stamped transactional data can be achieved by using the results of this procedure with the clustering procedures of SAS/STAT and the nodes of SAS Enterprise Miner.

The EXPAND procedure can be used for the frequency conversion and transformations of time series output from this procedure.