Time Series Analysis Package

Functional Summary

Note: Each function in this section has a prefix of "TSA."; however, the prefixes are omitted in descriptions for better readability.

Table 30 summarizes the functions in the TSA package.

Table 30: Functions in Time Series Analysis Package

TSA Function	Description
ACCUMULATE	Accumulates a univariate time series to a particular frequency
ACCUMULATE2	Accumulates a high-frequency time series to a lower frequency and expands the lower-frequency series to have the same length as the high-frequency series
ACF	Computes autocorrelation and autocovariance for a time series array
ARMAORDERS	Performs tests to tentatively identify the autoregressive and moving average orders of mixed autoregressive moving average models
CCF	Computes the cross-correlation and cross-covariance for two time series arrays
DPF Class Object	Performs count distribution analysis for time series
FREQ Class Object	Performs frequency analysis of a time series
INTERMITTENCYTEST	Tests for intermittency of a univariate time series
IACF	Computes the inverse autocorrelation for a time series array
MOVINGSUMMARY	Computes statistics for a set of values within a moving time window
PACF	Computes the partial autocorrelation for a time series array
SCALE	Scales a time series between the minimum value and the maximum value of the original time series
SEASONALDECOMP	Computes the seasonal indices of a univariate time series using classical decomposition
SEASONALINDICES	Computes the seasonal indices of a univariate time series by using regression seasonal dummies
SEASONTEST	Tests for seasonality of a univariate time series
SIMILARITY	Performs similarity analysis for time series
STATIONARITYTEST	Tests for stationarity of a univariate time series
TRANSFORM	Transforms time series according to the specified transformation type
UNBIASEDNESS	Tests whether a univariate time series is unbiased
WHITENOISE	Tests for white noise of a time series array

Last updated: July 09, 2026