Time Series Analysis > Building Workbook > Creating and Setting Up Calculated Series > Setting Up Calculation Method Parameters
Tool interfaces in the web application and in the desktop application are identical, and differences in functionality are given in appropiate commments.
To set up calculation method parameters of a calculated series, use the Series group of tabs on the side panel.
Make sure that the side panel is displayed.
Select a calculated series in the data table.
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.
A set of tabs depends on the selected calculation method. To work with most of the methods, use side panel tabs:
Parameters. It is used to set up general calculation parameters. Tab view depends on the calculation method, which is used.
Calculation Periods. It is used to edit the calculation period of the calculated series.
Missing Data Treatment. It is used to select a method of missing data treatment that is used to calculate missing values in variables.
Calculator is used to compose a custom formula, based on which a calculated series is calculated.
| Function | Brief description |
| Calculates absolute values of the series. | |
| Calculates the series logarithm by the specified value. | |
| Calculates the natural logarithm of the series. | |
| Calculates the series values rounded to integers. | |
| Calculates the remainder of the integer division of the series values into the specified number. | |
| Calculates the result of summation of two or more series. | |
| Calculates the result of multiplication of two or more series. | |
| Calculates the result of raising the number "e" to the power specified by series values. |
Function |
Brief description |
| Aggregates data by attribute values of time series. | |
| 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. | |
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Aggregation by time (sum). Aggregates data from the lower level to the upper by finding a sum of frequency elements values. |
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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 |
| It performs seasonal decomposition and adjustment of data using the Census2 method. NOTE. The X11 method is supported only in Windows. |
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| Smoothes the series by the Moving Average method. | |
| Smoothes the series by the Baxter-King Filter method. | |
| Smoothes the series by the Hodrick-Prescott Filter method. | |
| Smoothes a series by the Exponential Smoothing method. |
Time:
Function |
Brief description |
| Shifts a series forward by a specified number of points in a time period. | |
| Shifts a series backwards by a specified number of points in a time period. | |
| Splices several series or series parts. | |
| Truncates series by selected parameters. |
Mathematical:
Function |
Brief description |
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Growth coefficient (ratio PoP). Calculates the growth rate of series values in comparison with the previous values. |
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Growth coefficient (ratio YoY). Calculates the growth rate of series values in comparison with the respective values of the previous year. |
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Growth coefficient (ratio YTD). Calculates the growth rate of series values in comparison with the values of the end of the previous year. |
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Difference (diff PoP). Calculates the difference of the current and previous series value. |
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Difference (diff YoY). Calculates the difference between the current series value and the respective series value for the previous year. |
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Difference (diff YTD). Calculates the difference between the current series value and the series value for the end of the previous year. |
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Logarithms difference (dlog PoP). Calculates the difference of logarithms of the current series point and the previous series point. |
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Logarithms differenced (dlog YoY). Calculates the difference of logarithms of the current series value and the respective series value for the previous year. |
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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. |
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Rate of change (pch PoP). Calculates the rate of change of series values in percents compared with the previous values. |
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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. |
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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. |
| 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.
| Function | Brief description |
| Normalization | Normalizes series points. |
| Standardization | Standardizes series points. |
Function |
Brief description |
| Calculates the Linear Regression (OLS) method. | |
| Calculates the Linear Regression (IVM) method. | |
| Calculates the Error Correction Model model. | |
| Performs non-linear data transformations. |
NOTE. To use linear regression, execute integration with LPSolve.
| Function | Brief description |
| Models series values by trend method with automatic selection of optimal dependence type. | |
| Models series values using the ARIMA method. | |
| Models the series values using the Geometric Trend method. | |
| Models the series values using the Linear Trend method. | |
| Models the series values using the Logarithmic Parabolic Trend method. | |
| Models series values using the Grey Forecast method. | |
| Models the series values using the Inverse Trend method. | |
| Models the series values using the Parabolic Trend method. | |
| Models the series values using the Exponential Smoothing method. | |
| 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.
See also:
Working with Calculated Series
To display the group of tabs