You should create your own limits so you understand exactly how it works and can adjust it as needed.. You'll ned to knock the dust off those old statistics books and find the equations for the UCL and LCL and apply it here. I've posted about this a lot, here's an example This is the output of a query after the arguements have been passed into the macro, so some of the numerical values will represent a value you pass. These include, confidence interval | eval upper=if((count > pred),count,pred), lower=if((count < pred),count,pred), lower=if((lower == 0),"",lower) | eventstats avg(count) AS pred, stdev(count) as pred_stdev, by time, customer | eval upper=if((upper > (pred + (1 * pred_stdev))),((pred_stdev * 0.5) + pred),upper), lower=if((lower < (pred - (1 * pred_stdev))),((pred_stdev * 0.5) + pred),lower) | stats avg(count) AS pred, stdev(upper) AS ustdev, stdev(lower) AS lstdev stdev(count) as stdev by time, customer | eval low=(pred - (lstdev * (exact(3.1622776601683795)))), low=if((low < 0),1,low), high=(pred + (ustdev * (exact(3.1622776601683795)))), _time=time ... View more

Yes, the span is controlled by the span mentioned in your timechart command. If you write |timechart span=1d then when using your future_timespan="182" . It means that the future time span is 182 days. If you chose your timechart span to be 1 hour, the future_timespan would showcase 182 hours. etc ... View more

All the algorithms are based on the Kalman filter which is not proprietary. However, some of the variations we come up with are proprietary. Explanation of the Kalman filter can be found in the outside literature. One of the books I found useful while implementing the predict command is "An Introduction to State Space Time Series Analysis" by Commandeur-Koopman. Local Level (LL): this a univariate model with no trends and no seasonaility. Seasonal Local Level (LLP): this is a univariate model with seasonality. The periodicity of the time series is automatically computed. Local Level Trend (LLT): this is a univariate model with trend but no seasonality. Bivariate Local Level (LLB): this is a bivariate model with no trends and no seasonality. The lower 95 and upper 95 specifies a confidence interval in which we expect 95% of the predictions to fall. Most of the time SOME of the predictions will fall outside the confidence interval. That is normal because 1. the confidence interval does not cover 100% of the predictions and 2. the confidence interval is about a probabilistic expectation and things don't match the expectation exactly. ... View more