Plugins
TsARSink
@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsARSink")
@Description("A building stage for an Apache Spark based AR model for time series datasets.")
public class TsARSink extends ARSink {
...
}
Parameters
| Model Name | The unique name of the AR 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). |
| 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'. |
TsAR
@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsAR")
@Description("A prediction stage that leverages a trained Apache Spark based AR time series model.")
public class TsAR extends ARCompute {
...
}
Parameters
| Model Name | The unique name of the AR 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. |
TsAutoARSink
@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsAutoARSink")
@Description("A building stage for an Apache Spark based AutoAR model for time series datasets.")
public class TsAutoARSink extends ARSink {
...
}
Parameters
| Model Name | The unique name of the AutoAR 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). |
| 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'. |
TsAutoAR
@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsAutoAR")
@Description("A prediction stage that leverages a trained Apache Spark based AutoAR time series model.")
public class TsAutoAR extends ARCompute {
...
}
Parameters
| Model Name | The unique name of the AutoAR 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. |
TsDiffARSink
@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsDiffARSink")
@Description("A building stage for an Apache Spark based Differencing AR model for time series datasets.")
public class TsDiffARSink extends ARSink {
...
}
Parameters
| Model Name | The unique name of the Differencing AR 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). |
| Differencing Degree | The positive number of times that the raw observations are differenced (also called the degree of differencing). |
| 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. |
TsDiffAR
@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsDiffAR")
@Description("A prediction stage that leverages a trained Apache Spark based Differencing AR time series model.")
public class TsDiffAR extends ARCompute {
...
}
Parameters
| Model Name | The unique name of the Differencing AR 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. |
TsYuleWalkerSink
@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TsYuleWalkerSink")
@Description("A building stage for an Apache Spark based Yule Walker model for time series datasets.")
public class TsYuleWalkerSink extends ARSink {
...
}
Parameters
| Model Name | The unique name of the Yule Walker 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). |
TsYuleWalker
@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TsYuleWalker")
@Description("A prediction stage that leverages a trained Apache Spark based Yule Walker AR time series model.")
public class TsYuleWalker extends ARCompute {
...
}
Parameters
| Model Name | The unique name of the Yule Walker 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. |