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streamingtimeseries properties

streamingtimeseries properties

Streaming TS node iconThe Streaming Time Series node builds and scores time series models in one step.

Example

stream = modeler.script.stream()
typenode = stream.findByID("id42KW3MSA94B")
node = stream.createAt("ts_streaming", "Streaming Time Series", 200, 200)
stream.link(typenode, node)
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("arima", [1,2,3,4,5,6])
node.setPropertyValue("candidate_inputs", ["Na", "K"])
node.setPropertyValue("targets", ["Age"])
node.setKeyedPropertyValue("tf_arima", "Na", [1,2,3,4,5,6,10,"None"])
Table 1. streamingtimeseries properties
streamingtimeseries properties Values Property description
targets field The Streaming TS node forecasts one or more targets, optionally using one or more input fields as predictors. Frequency and weight fields aren't used. See Common modeling node properties for more information.
candidate_inputs [field1 ... fieldN] Input or predictor fields used by the model.
use_period flag  
date_time_field field  
input_interval None Unknown Year Quarter Month Week Day Hour Hour_nonperiod Minute Minute_nonperiod Second Second_nonperiod  
period_field field  
period_start_value integer  
num_days_per_week integer  
start_day_of_week Sunday Monday Tuesday Wednesday Thursday Friday Saturday  
num_hours_per_day integer  
start_hour_of_day integer  
timestamp_increments integer  
cyclic_increments integer  
cyclic_periods list  
output_interval None Year Quarter Month Week Day Hour Minute Second  
is_same_interval flag  
cross_hour flag  
aggregate_and_distribute list  
aggregate_default Mean Sum Mode Min Max  
distribute_default Mean Sum  
group_default Mean Sum Mode Min Max  
missing_imput Linear_interp Series_mean K_mean K_median Linear_trend  
k_span_points integer  
use_estimation_period flag  
estimation_period Observations Times  
date_estimation list Only available if you use date_time_field.
period_estimation list Only available if you use use_period.
observations_type Latest Earliest  
observations_num integer  
observations_exclude integer  
method ExpertModeler Exsmooth Arima  
expert_modeler_method ExpertModeler Exsmooth Arima  
consider_seasonal flag  
detect_outliers flag  
expert_outlier_additive flag  
expert_outlier_innovational flag  
expert_outlier_level_shift flag  
expert_outlier_transient flag  
expert_outlier_seasonal_additive flag  
expert_outlier_local_trend flag  
expert_outlier_additive_patch flag  
consider_newesmodels flag  
exsmooth_model_type Simple HoltsLinearTrend BrownsLinearTrend DampedTrend SimpleSeasonal WintersAdditive WintersMultiplicative DampedTrendAdditive DampedTrendMultiplicative MultiplicativeTrendAdditive MultiplicativeSeasonal MultiplicativeTrendMultiplicative MultiplicativeTrend  
futureValue_type_method Compute specify  
exsmooth_transformation_type None SquareRoot NaturalLog  
arima.p integer  
arima.d integer  
arima.q integer  
arima.sp integer  
arima.sd integer  
arima.sq integer  
arima_transformation_type None SquareRoot NaturalLog  
arima_include_constant flag  
tf_arima.p. fieldname integer For transfer functions.
tf_arima.d. fieldname integer For transfer functions.
tf_arima.q. fieldname integer For transfer functions.
tf_arima.sp. fieldname integer For transfer functions.
tf_arima.sd. fieldname integer For transfer functions.
tf_arima.sq. fieldname integer For transfer functions.
tf_arima.delay. fieldname integer For transfer functions.
tf_arima.transformation_type. fieldname None SquareRoot NaturalLog For transfer functions.
arima_detect_outliers flag  
arima_outlier_additive flag  
arima_outlier_level_shift flag  
arima_outlier_innovational flag  
arima_outlier_transient flag  
arima_outlier_seasonal_additive flag  
arima_outlier_local_trend flag  
arima_outlier_additive_patch flag  
conf_limit_pct real  
events fields  
forecastperiods integer  
extend_records_into_future flag  
conf_limits flag  
noise_res flag  
max_models_output integer Specify the maximum number of models you want to include in the output. Note that if the number of models built exceeds this threshold, the models aren't shown in the output but they're still available for scoring. Default value is 10. Displaying a large number of models may result in poor performance or instability.
custom_fields boolean This option tells the node to use the field information specified here instead of that given in any upstream Type node(s). After selecting this option, specify the following fields as required.
arima array A list with p, d, q, sp, sd, sq.
tf_arima array A list with name, p, q, d, sp, sq, sd, delay and type.
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