The EXPAND procedure provides time interval conversion and missing value interpolation for time series. The EXPAND procedure includes the following features:
conversion of time series frequency; for example, constructing quarterly estimates from annual series or aggregating quarterly values to annual values
conversion of irregular observations to periodic observations
interpolation of missing values in time series
conversion of observation types; for example, estimate stocks from flows and vice versa. All possible conversions are supported between any of the following:
beginning of period
end of period
period midpoint
period total
period average
conversion of time series phase shift; for example, conversion between fiscal years and calendar years
identifying observations including the following:
identification of the time interval of the input values
validation of the input data set observations
computation of the ID values for the observations in the output data set
choice of four interpolation methods:
cubic splines
linear splines
step functions
simple aggregation
ability to perform extrapolation by a linear projection of the trend of the cubic spline curve fit to the input data
ability to transform series before and after interpolation (or without interpolation) by using any of the following:
constant shift or scale
sign change or absolute value
logarithm, exponential, square root, square, logistic, inverse logistic
lags, leads, differences
classical decomposition
bounds, trims, reverse series
centered moving, cumulative, or backward moving average
centered moving, cumulative, or backward moving range
centered moving, cumulative, or backward moving geometric mean
centered moving, cumulative, or backward moving maximum
centered moving, cumulative, or backward moving median
centered moving, cumulative, or backward moving minimum
centered moving, cumulative, or backward moving product
centered moving, cumulative, or backward moving corrected sum of squares
centered moving, cumulative, or backward moving uncorrected sum of squares
centered moving, cumulative, or backward moving rank
centered moving, cumulative, or backward moving standard deviation
centered moving, cumulative, or backward moving sum
centered moving, cumulative, or backward moving median
centered moving, cumulative, or backward moving t-value
centered moving, cumulative, or backward moving variance
support for a wide range of time series frequencies:
YEAR
SEMIYEAR
QUARTER
MONTH
SEMIMONTH
TENDAY
WEEK
WEEKDAY
DAY
HOUR
MINUTE
SECOND
support for repeating of shifting the basic interval types to define a great variety of different frequencies, such as fiscal years, biennial periods, work shifts, and so forth
Refer to ChapterĀ 3: Working with Time Series Data, and ChapterĀ 4: Date Intervals, Formats, and Functions, for more information about time series data transformations.