StatefulExpr

Type Members

1. case class Delay(expr: TimeSeriesExpr, n: Int) extends OnlineExpr with Product with Serializable

   

   Delay the input time series by n intervals.

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

2. case class Derivative(expr: TimeSeriesExpr) extends OnlineExpr with Product with Serializable

   

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

3. case class Des(expr: TimeSeriesExpr, trainingSize: Int, alpha: Double, beta: Double) extends OnlineExpr with Product with Serializable

   

   DES expression.

   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.

4. case class Integral(expr: TimeSeriesExpr) extends OnlineExpr with Product with Serializable

   

   Sum the values across the evaluation context.

   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.

5. trait OnlineExpr extends StatefulExpr

   

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

6. case class RollingCount(expr: TimeSeriesExpr, n: Int) extends OnlineExpr with Product with Serializable

   

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

7. case class RollingMax(expr: TimeSeriesExpr, n: Int) extends OnlineExpr with Product with Serializable

   

   Computes the maximum value over the last n intervals.

8. case class RollingMean(expr: TimeSeriesExpr, n: Int, minNumValues: Int) extends OnlineExpr with Product with Serializable

   

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

9. case class RollingMin(expr: TimeSeriesExpr, n: Int) extends OnlineExpr with Product with Serializable

   

   Computes the minimum value over the last n intervals.

10. case class RollingSum(expr: TimeSeriesExpr, n: Int) extends OnlineExpr with Product with Serializable

    

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

11. case class SlidingDes(expr: TimeSeriesExpr, trainingSize: Int, alpha: Double, beta: Double) extends OnlineExpr with Product with Serializable

    

    Sliding DES expression.

    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.

12. case class Trend(expr: TimeSeriesExpr, window: Duration) extends OnlineExpr with Product with Serializable

    

    Compute a moving average for values within the input time series.

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