Introduction |

Time Series Interpolation and Frequency Conversion |

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*-valuecentered 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 Chapter 3, Working with Time Series Data, and Chapter 4, Date Intervals, Formats, and Functions, for more information about time series data transformations.

Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.