Plugins

TsARMASink

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsARMASink")
@Description("A building stage for an Apache Spark based ARMA model for time series datasets.")
public class TsARMASink extends ARMASink {

    ...

}

Parameters

Model Name The unique name of the ARMA model.
Time Field The name of the field in the input schema that contains the time value.
Value Field The name of the field in the input schema that contains the value.
Time Split The split of the dataset into train & test data, e.g. 80:20. Note, this is a split time and is computed from the total time span (min, max) of the time series. Default is 70:30."
Model Configuration
Lag Order The positive number of lag observations included in the time series model (also called the lag order).
Moving Average Order A positive number that specifies the size of the moving average window (also called the order of moving average).
ElasticNet Mixing The ElasticNet mxing parameter. For value = 0.0, the penalty is an L2 penalty. For value = 1.0, it is an L1 penalty. For 0.0 < value < 1.0, the penalty is a combination of L1 and L2. Default is 0.0.
Regularization Parameter The nonnegative regularization parameter. Default is 0.0.
Standardization The indicator to determine whether to standardize the training features before fitting the model. Default is 'true'."
With Intercept The indicator to determine whether to fit an intercept value.
Remove Mean The indicator to determine whether to remove the mean value from the value from the value of the time series before training model. Default is 'false'.

TsARMA

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsARMA")
@Description("A transformation stage that leverages a trained ARMA model to look n steps in time ahead. "
		+ "The forecast result is described by a two column output schema, one column specifies the future "
		+ "points in time, and another the forecasted values.")
public class TsARMA extends ARMACompute {

    ...

}

Parameters

Model Name The unique name of the ARMA model.
Time Field The name of the field in the input schema that contains the time value.
Value Field The name of the field in the input schema that contains the value.
Time Steps The positive number of discrete time steps to look ahead. Default is 1.

TsAutoARMASink

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsAutoARMASink")
@Description("A building stage for an Apache Spark based AutoARMA model for time series datasets.")
public class TsAutoARMASink extends ARMASink {

    ...

}

Parameters

Model Name The unique name of the AutoARMA model.
Time Field The name of the field in the input schema that contains the time value.
Value Field The name of the field in the input schema that contains the value.
Time Split The split of the dataset into train & test data, e.g. 80:20. Note, this is a split time and is computed from the total time span (min, max) of the time series. Default is 70:30."
Model Configuration
Maximum Lag Order The positive upper limit for tuning the number of lag operations (p).
Maximum Moving Average Order The positive upper limit for tuning the size of the moving average window (q).
ElasticNet Mixing The ElasticNet mxing parameter. For value = 0.0, the penalty is an L2 penalty. For value = 1.0, it is an L1 penalty. For 0.0 < value < 1.0, the penalty is a combination of L1 and L2. Default is 0.0.
Regularization Parameter The nonnegative regularization parameter. Default is 0.0.
Standardization The indicator to determine whether to standardize the training features before fitting the model. Default is 'true'."
With Intercept The indicator to determine whether to fit an intercept value.
Remove Mean The indicator to determine whether to remove the mean value from the value from the value of the time series before training model. Default is 'false'.
Info Criterion The information criterion to calculate for model parameter tuning. Supported values are 'aic' (Akaike Information Criterion), 'aicc' (AIC with correction for finite sample sizes) and 'bic' (Bayesian Information Criterion). Default is 'aic'.

TsAutoARMA

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsAutoARMA")
@Description("A transformation stage that leverages a trained AutoARMA model to look n steps in time ahead. "
		+ "The forecast result is described by a two column output schema, one column specifies the future "
		+ "points in time, and another the forecasted values.")
public class TsAutoARMA extends ARMACompute {

    ...

}

Parameters

Model Name The unique name of the AutoARMA model.
Time Field The name of the field in the input schema that contains the time value.
Value Field The name of the field in the input schema that contains the value.
Time Steps The positive number of discrete time steps to look ahead. Default is 1.