# series_fit_line()

Applies linear regression on a series, returning multiple columns.

Takes an expression containing dynamic numerical array as input and performs linear regression in order to find the line that best fits it. This function should be used on time series arrays, fitting the output of make-series operator. It generates the following columns:

• `rsquare`: r-square is a standard measure of the fit quality. It's a number in the range [0-1], where 1 - is the best possible fit, and 0 means the data is totally unordered and do not fit any line
• `slope`: slope of the approximated line (this is a from y=ax+b)
• `variance`: variance of the input data
• `rvariance`: residual variance which is the variance between the input data values the approximated ones.
• `interception`: interception of the approximated line (this is b from y=ax+b)
• `line_fit`: numerical array holding a series of values of the best fitted line. The series length is equal to the length of the input array. It is mainly used for charting.

Syntax

`series_fit_line(`x`)`

Arguments

• x: Dynamic array of numeric values.

Tip

The most convenient way of using this function is applying it to the results of make-series operator.

Examples

``````print id=' ', x=range(bin(now(), 1h)-11h, bin(now(), 1h), 1h), y=dynamic([2,5,6,8,11,15,17,18,25,26,30,30])
| extend (RSquare,Slope,Variance,RVariance,Interception,LineFit)=series_fit_line(y)
| render timechart
`````` RSquare Slope Variance RVariance Interception LineFit
0.982 2.730 98.628 1.686 -1.666 1.064, 3.7945, 6.526, 9.256, 11.987, 14.718, 17.449, 20.180, 22.910, 25.641, 28.371, 31.102