@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsAggregate")
@Description("A time series aggregation stage that aggregates a sparse time series leveraging a user-defined "
		+ "tumbling window. Suppose a certain point in time refers to 09:00 am and a window of 10 minutes is "
		+ "defined, then all points in time falling into the window [09:00, 09:10] are collected and their "
		+ "associated values aggregated.")
public class TsAggregate extends TimeCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsACF")
@Description("A time series computation stage that determines the ACF (Auto Correlation Function) "
    + "of a time series. This stage does not transform the input dataset, but computes and persists "
    + "its ACF for ease of use in subsequent stages.")
public class TsACF extends TimeCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsInterpolate")
@Description("A time series interpolation stage that adds missing values. This stage "
    + "interpolates missing values from the last non-null value before and the first "
    + "on-null value after the respective null value.")
public class TsInterpolate extends TimeCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsResample")
@Description("A time series resampling stage that turns a sparse time series into "
    + "an equidistant time grid. This stage supports data values that belong to "
    + "different categories or groups, say sensor devices. Then resampling is performed "
    + "on a per group basis. Note, resampling may lead to missing values for intermediate "
    + "points in time.")
public class TsResample extends TimeCompute {

    ...

}