tcmnode properties | IBM Cloud Pak for Data as a Service

tcmnode properties

Last updated: Jan 17, 2024

tcmnode properties

TCM node icon Temporal causal modeling attempts to discover key causal relationships in time series data. In temporal causal modeling, you specify a set of target series and a set of candidate inputs to those targets. The procedure then builds an autoregressive time series model for each target and includes only those inputs that have the most significant causal relationship with the target.

Table 1. tcmnode properties
`tcmnode` Properties	Values	Property description
`custom_fields`	Boolean
`dimensionlist`	[dimension1 ... dimensionN]
`data_struct`	`Multiple` `Single`
`metric_fields`	fields
`both_target_and_input`	[f1 ... fN]
`targets`	[f1 ... fN]
`candidate_inputs`	[f1 ... fN]
`forced_inputs`	[f1 ... fN]
`use_timestamp`	`Timestamp` `Period`
`input_interval`	`None` `Unknown` `Year` `Quarter` `Month` `Week` `Day` `Hour` `Hour_nonperiod` `Minute` `Minute_nonperiod` `Second` `Second_nonperiod`
`period_field`	string
`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`	`Same` `Notsame`
`cross_hour`	Boolean
`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_meridian` `Linear_trend` `None`
`k_mean_param`	integer
`k_median_param`	integer
`missing_value_threshold`	integer
`conf_level`	integer
`max_num_predictor`	integer
`max_lag`	integer
`epsilon`	number
`threshold`	integer
`is_re_est`	Boolean
`num_targets`	integer
`percent_targets`	integer
`fields_display`	list
`series_dispaly`	list
`network_graph_for_target`	Boolean
`sign_level_for_target`	number
`fit_and_outlier_for_target`	Boolean
`sum_and_para_for_target`	Boolean
`impact_diag_for_target`	Boolean
`impact_diag_type_for_target`	`Effect` `Cause` `Both`
`impact_diag_level_for_target`	integer
`series_plot_for_target`	Boolean
`res_plot_for_target`	Boolean
`top_input_for_target`	Boolean
`forecast_table_for_target`	Boolean
`same_as_for_target`	Boolean
`network_graph_for_series`	Boolean
`sign_level_for_series`	number
`fit_and_outlier_for_series`	Boolean
`sum_and_para_for_series`	Boolean
`impact_diagram_for_series`	Boolean
`impact_diagram_type_for_series`	`Effect` `Cause` `Both`
`impact_diagram_level_for_series`	integer
`series_plot_for_series`	Boolean
`residual_plot_for_series`	Boolean
`forecast_table_for_series`	Boolean
`outlier_root_cause_analysis`	Boolean
`causal_levels`	integer
`outlier_table`	`Interactive` `Pivot` `Both`
`rmsp_error`	Boolean
`bic`	Boolean
`r_square`	Boolean
`outliers_over_time`	Boolean
`series_transormation`	Boolean
`use_estimation_period`	Boolean
`estimation_period`	`Times` `Observation`
`observations`	list
`observations_type`	`Latest` `Earliest`
`observations_num`	integer
`observations_exclude`	integer
`extend_records_into_future`	Boolean
`forecastperiods`	integer
`max_num_distinct_values`	integer
`display_targets`	`FIXEDNUMBER` `PERCENTAGE`
`goodness_fit_measure`	`ROOTMEAN` `BIC` `RSQUARE`
`top_input_for_series`	Boolean
`aic`	Boolean
`rmse`	Boolean
`date_time_field`	field	Time/Date field
`auto_detect_lag`	Boolean	This setting specifies the number of lag terms for each input in the model for each target.
`numoflags`	Integer	By default, the number of lag terms is automatically determined from the time interval that is used for the analysis.
`re_estimate`	Boolean	If you already generated a temporal causal model, select this option to reuse the criteria settings that are specified for that model, rather than building a new model.
`display_targets`	`"FIXEDNUMBER"` `"PERCENTAGE"`	By default, output is displayed for the targets that are associated with the 10 best-fitting models, as determined by the R square value. You can specify a different fixed number of best-fitting models or you can specify a percentage of best-fitting models.