Plugins
TsMASink
@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsMASink")
@Description("A building stage for an Apache Spark based MA model for time series datasets.")
public class TsMASink extends MASink {
...
}
Parameters
| Model Name | The unique name of the MA 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 | |
| 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'. |
TsMA
@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsMA")
@Description("A prediction stage that leverages a trained Apache Spark based MA time series model.")
public class TsMA extends MACompute {
...
}
Parameters
| Model Name | The unique name of the MA 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. |
TsAutoMASink
@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsAutoMASink")
@Description("A building stage for an Apache Spark based AutoMA model for time series datasets.")
public class TsAutoMASink extends MASink {
...
}
Parameters
| Model Name | The unique name of the AutoMA 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 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'. |
TsAutoMA
@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsAutoMA")
@Description("A prediction stage that leverages a trained Apache Spark based AutoMA time series model.")
public class TsAutoMA extends MACompute {
...
}
Parameters
| Model Name | The unique name of the AutoMA 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. |