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-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 Chapter 4: Working with Time Series Data, and Chapter 5: Date Intervals, Formats, and Functions, for more information about time series data transformations.