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 parameters details window size this parameter determines how many values the processor should observe before making each prediction number of predictions this parameter specifies how many polling intervals into the future the processor should predict polling interval this parameter defines the time interval, in seconds, between successive data polling or data collection timer interval if no event is detected, this timer interval is used to keep displaying the output continually entering zero disables this timer, and the prediction model relies entirely on incoming data for updates note when creating an analytics flow with linear prediction processor, refer the docid 8o1rvkxdb3f1q0pnlh8jr guide for more details