tcmnode Properties

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_display`	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