Setting Up Calculation Method Parameters

1. Make sure that the side panel is displayed.

2. Select a calculated series in the data table.

3. Select the Series radio button on the side panel.

TIP. For quick displaying of the Series group of tabs on the side panel, select the Show Parameters item in the context menu of the selected series.

Calculator is used to compose a custom formula, based on which a calculated series is calculated.

Function	Brief description
Absolute value	Calculates absolute values of the series.
Logarithm	Calculates the series logarithm by the specified value.
Natural logarithm	Calculates the natural logarithm of the series.
Rounding	Calculates the series values rounded to integers.
Mod	Calculates the remainder of the integer division of the series values into the specified number.
Summation	Calculates the result of summation of two or more series.
Multiplication	Calculates the result of multiplication of two or more series.
Exponent	Calculates the result of raising the number "e" to the power specified by series values.

Function	Brief description
Aggregation by indicators	Aggregates data by attribute values of time series.
Aggregation by time	Aggregates data by levels of calendar frequency of time series.
	Aggregation by time (minimum). Aggregates data from the lower level to the upper by finding a minimum of frequency elements values.
	Aggregation by time (maximum). Aggregates data from the lower level to the upper by finding a maximum of frequency elements values.
	Aggregation by time (first element). Aggregates data from the lower level to the upper by finding the first available value of frequency elements.
	Aggregation by time (last element). Aggregates data from the lower level to the upper by finding the last available value of frequency elements.
	Aggregation by time (average). Aggregates data from the lower level to the upper by finding an average of frequency elements values.
	Aggregation by time (standard deviation). Aggregates data from the lower level to the upper by finding the frequency standard deviation.
	Aggregation by time (sum). Aggregates data from the lower level to the upper by finding a sum of frequency elements values.
Interpolation by time
	Interpolation by time (repeat). Disaggregates data from the upper level to the lower using the repeated interpolation.
	Interpolation by time (uniform). Disaggregates data from the upper level to the lower using the proportional interpolation.

NOTE. The Aggregation by Indicators method is available only in the desktop application.

Function	Brief description
Seasonal adjustment (X11)	It performs seasonal decomposition and adjustment of data using the Census2 method. NOTE. The X11 method is supported only in Windows.
Moving average	Smoothes the series by the Moving Average method.
Baxter-King filter	Smoothes the series by the Baxter-King Filter method.
Hodrick-Prescott filter	Smoothes the series by the Hodrick-Prescott Filter method.
Exponential smoothing	Smoothes a series by the Exponential Smoothing method.

Time:

Function	Brief description
Lag	Shifts a series forward by a specified number of points in a time period.
Lead	Shifts a series backwards by a specified number of points in a time period.
Splice	Splices several series or series parts.
Truncate	Truncates series by selected parameters.

Mathematical:

Function	Brief description
Growth coefficient
	Growth coefficient (ratio PoP). Calculates the growth rate of series values in comparison with the previous values.
	Growth coefficient (ratio YoY). Calculates the growth rate of series values in comparison with the respective values of the previous year.
	Growth coefficient (ratio YTD). Calculates the growth rate of series values in comparison with the values of the end of the previous year.
Difference
	Difference (diff PoP). Calculates the difference of the current and previous series value.
	Difference (diff YoY). Calculates the difference between the current series value and the respective series value for the previous year.
	Difference (diff YTD). Calculates the difference between the current series value and the series value for the end of the previous year.
Logarithms difference
	Logarithms difference (dlog PoP). Calculates the difference of logarithms of the current series point and the previous series point.
	Logarithms differenced (dlog YoY). Calculates the difference of logarithms of the current series value and the respective series value for the previous year.
	Logarithms difference (dlog YTD). Calculates the difference of logarithms of the current series value and the series value for the end of the previous year.
Rate of change
	Rate of change (pch PoP). Calculates the rate of change of series values in percents compared with the previous values.
	Rate of change (pch YoY). Calculates the rate of change of series values in percentage in comparison with the respective values of the previous year.
	Rate of change (pch YTD). Calculates the rate of change of series values in percents in comparison with the values of the end of previous year.

Cumulative methods:

Function	Brief description
Cumulative maximum	Transforms data using the Maximum cumulative method.
Cumulative median	Transforms data using the Median cumulative method.
Cumulative minimum	Transforms data using the Minimum cumulative method.
Cumulative average	Transforms data using the Average cumulative method.
Cumulative standard deviation	Transforms data using the Standard Deviation cumulative method.
Cumulative sum	Transforms data using the Sum cumulative method.
Cumulative multiply	Transforms data using the Product cumulative method.

Methods of missing data substitution:

Function	Brief description
Geometric interpolation	Empty values of series are calculated using geometric interpolation.
Cubic spline interpolation	Empty values of series are calculated using cubic spline interpolation.
Linear interpolation	Empty values of series are calculated using linear interpolation by two neighbor points.
Linear trend	Empty values of series are calculated using linear trend.
Previous value	Empty values of series are replaced with the previous non-empty value.
Succeeding value	Empty values of series are replaced with the succeeding non-empty value.
Average	Empty values of series are calculated as average.
N-point average	Empty values of series are calculated as N-point average.
Previous growth rate	Empty values of series are calculated based on growth rate of existing values in comparison with the previous period.
Succeeding growth rate	Empty values of series are calculated based on growth rate of existing values in comparison with the succeeding period.
Value	Empty values of series are replaced with the specified number.

NOTE. The Linear Trend, Average, N-Point Average methods are available only in the desktop application.

Statistical:

Function	Brief description
Normalization	Normalizes series points.
Standardization	Standardizes series points.

Function	Brief description
Linear regression (OLS)	Calculates the Linear Regression (OLS) method.
Linear regression (IVM)	Calculates the Linear Regression (IVM) method.
Error correction model	Calculates the Error Correction Model model.
Non-linear regression	Performs non-linear data transformations.

NOTE. To use linear regression, execute integration with LPSolve.

Function	Brief description
Autotrend	Models series values by trend method with automatic selection of optimal dependence type.
ARIMA	Models series values using the ARIMA method.
Geometric trend	Models the series values using the Geometric Trend method.
Linear trend	Models the series values using the Linear Trend method.
Logarithmic parabolic trend	Models the series values using the Logarithmic Parabolic Trend method.
Grey forecast	Models series values using the Grey Forecast method.
Inverse trend	Models the series values using the Inverse Trend method.
Parabolic trend	Models the series values using the Parabolic Trend method.
Exponential smoothing (forecast)	Models the series values using the Exponential Smoothing method.
Exponential trend	Models the series values using the Exponential Trend method.

R methods enable the user to create calculated series using the R package.

NOTE. Methods are available if Foresight Analytics Platform has R package connected. For details see the How to Set Up Integration with R? section.

Custom functions extend time series analysis functionality due to the use of custom calculation methods.