Abbreviation: LinTimeReg PredChange
Category: Regression
Input Parameters:
| Name | Range | Default |
| Time Series | Close | |
| Regression Periods | Int >= 3 | 10 |
| Periods Ahead | Integer | 1 |
Calculation:
YIntercept + Slope(Y, n) * (Current Time Period + Periods Ahead) – Current Y
where
YIntercept = mean(Y) – Slope(Y, n) * mean(X)
mean(X) = mean average of X over the last n periods
mean(Y) = mean average of Y over the last n periods
X = Period Number
Y = Time Series
n = Regression Periods
Slope represents Linear Time Regression: Coefficient of Regression (Slope)
Discussion:
Computes the change in the time series value (future value – current value) predicted by the linear regression line, where the prediction is for the specified number of periods into the future (or past if periods ahead is less than 0). Note that the linear regression line is the straight line that provides the best fit to the time series points (y axis) and their corresponding time period (x axis) over the last n periods.