Linear Prediction

the linear prediction processor forecast future data points by fitting a linear equation to collected data and using statistical techniques to identify the underlying trend linear prediction overview once window values are full, the mean of values is subtracted from each element and squared (y i mean y) same is done for the time series elements (x i mean x) the sum of squares is sum of (x i mean x)^2 the sum of products is sum of (y i mean y) (x i mean x) the slope is obtained by the formula m = { sum of products } / { sum of squares } the intercept is obtained by the formula b = mean y m mean x finally, prediction is made by extrapolating the number of predictions in the simple formula y = m x + b residual error is obtained by modeling for current value, and subtracting the actual current value the timer interval parameter is useful if the connected input tag is currently not polling, but you still want this kpi to publish a value every few seconds, defined by the aforementioned timer if you know your input is going to publish at the expected interval, it is better to disable this timer by entering 0 in the field linear prediction parameters true false 182false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type note when creating an analytics flow with linear prediction processor, refer the use the linear prediction function docid 8o1rvkxdb3f1q0pnlh8jr guide for more details