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Trend/Aggregation Types


IBM Alphablox offer the following trend types:

Type

Description

Equation

Linear

The least squares fit for a line represented.

This is the equation for linear trendlines.
where C1 is the slope and C0 is the intercept.

Logarithmic

The least squares fit through the data points.

The is the equation for logarithmic trendlines.
where C0 and C1 are constants, and ln is the natural logarithm function.

Polynomial

The least squares fit through the data points.

This is the equation for polynomial trendlines.
where C0 and C1...Cn are constants, where 2 The less or equal signn The less or equal sign100.

Power

The least squares fit through the data points.

This is the equation for a power trendline.
where c and b are constants

Exponential

The least squares fit through the data points.

This is the equation for exponential trendlines.
where c and b are constants, and e is the base of the natural logarithm.

Moving Average

The average over the a specified time period. The number of periods in a moving average trendline equals the total number of points in the series less the number you specify for the period.

This is the equation for a trendline that shows the moving averages.
where N is the number of prior periods to include in the moving average; Aj is the actual value at time j; Fj is the forecasted value at time j.

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