StatefulExpr

Delay the input time series by n intervals. This can be useful for alerting to see if recent trends deviate from delayed trends.

Determine the rate of change per step interval for the input time series.

DES expression. In order to get the same results, it must be replayed from the same starting point. Used sliding DES if deterministic results are important.

Sum the values across the evaluation context. This is typically used to approximate the distinct number of events that occurred. If the input is non-negative, then each datapoint for the output line will represent the area under the input line from the start of the graph to the time for that datapoint. Missing values, NaN, will be treated as zeroes.

Base type for stateful expressions that are based on an implementation of OnlineAlgorithm.

Computes the number of true values over the last n intervals.

Computes the maximum value over the last n intervals.

Computes the mean of the values over the last n intervals.

Computes the minimum value over the last n intervals.

Computes the sum of the values over the last n intervals.

Sliding DES expression. In order to keep the values deterministic the start time must be aligned to a step boundary. As a result, the initial gap before predicted values start showing up will be the offset to align to a step boundary plus the training window.

Compute a moving average for values within the input time series. The duration will be rounded down to the nearest step boundary.