Trend line refers to a straight or a curved line that is superimposed on the chart to inform user about direction of the data or the trend, and hence helps in predicting the future values. Due to ability to depict future prices, trend lines are often used in trade analysis to understand the price movement and forecast the value of securities.

FlexChart supports regression as well as non-regression trend lines and the fit type and order of these lines can be specified using the FitType property and Order property of this class respectively. Following is the list of various fit types supported in the FlexChart control:

TrendLine.FitType	Snapshot	Description
Linear		A linear trend line is the straight line that most closely approximates the data in the chart. The data is linear, if the data pattern resembles a line. Equation - Y(x) = C0 + C1*x
Polynomial		Polynomial trend lines are curved lines that are used with fluctuating data. They are useful for analyzing gains or losses over a large data set. When using a polynomial trend line, it is important to also set the Order of the line, which can be determined by the number of fluctuations in the data. Equation - Y(x) = C0 + C1*x + C2*x2 + : + Cn-1*xn-1
Logarithmic		Logarithmic trend line is a best-fit curved line that is most useful when the rate of change in the data increases or decreases quickly and then levels out. A logarithmic trend line can use negative and/or positive values. Equation - Y(x) = C0 * ln(C1*x)
Power		Power trend line is a curved line that is best used with data sets that compare measurements that increase at a specific rate — for example, the acceleration of a race car at one-second intervals. You cannot create a power trend line if your data contains zero or negative values. Equation - Y(x) = C0 * pow(x, C1)
Exponent		Exponential trend line is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trend line if your data contains zero or negative values. Equation - Y(x) = C0 * exp( C1*x)
Fourier		Fourier trend line identifies patterns or cycles in a series data set. It removes the effects of trends or other complicating factors from the data set, thereby providing a good estimate of the direction that the data under analysis will take in the future. Equation - Y(x) = C0 + C1 * cos(x) + C2 * sin(x) + C3 * cos(2*x) + C4 * sin(2*x) + ...
MinX		The minimum X-value on the chart.
MinY		The minimum Y-value on the chart.
MaxX		The maximum X-value on the chart.
MaxY		The maximum Y-value on the chart.
AverageX		The average X-value on the chart.
AverageY		The average Y-value on the chart.