ts properties | IBM Cloud Pak for Data as a Service

ts properties

Time Series node icon The Time Series node estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function) models for time series data and produces forecasts of future performance.

Table 1. ts properties
`ts` Properties	Values	Property description
`targets`	field	The Time Series node forecasts one or more targets, optionally using one or more input fields as predictors. Frequency and weight fields are not 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_level_shift`	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`	Specifies the Exponential Smoothing method. Default is `Simple`.
`futureValue_type_method`	`Compute` `specify`	If `Compute` is used, the system computes the Future Values for the forecast period for each predictor. For each predictor, you can choose from a list of functions (blank, mean of recent points, most recent value) or use `specify` to enter values manually. To specify individual fields and properties, use the `extend_metric_values` property. For example: `set :ts.futureValue_type_method="specify" set :ts.extend_metric_values=[{'Market_1','USER_SPECIFY', [1,2,3]}, {'Market_2','MOST_RECENT_VALUE', ''},{'Market_3','RECENT_POINTS_MEAN', ''}]`
`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
`max_lags`	integer
`cal_PI`	flag
`conf_limit_pct`	real
`events`	fields
`continue`	flag
`scoring_model_only`	flag	Use for models with very large numbers (tens of thousands) of time series.
`forecastperiods`	integer
`extend_records_into_future`	flag
`extend_metric_values`	fields	Allows you to provide future values for predictors.
`conf_limits`	flag
`noise_res`	flag
`max_models_output`	integer	Controls how many models are shown in output. Default is `10`. Models are not shown in output if the total number of models built exceeds this value. Models are still available for scoring.
`missing_value_threshold`	double	Computes data quality measures for the time variable and for input data corresponding to each time series. If the data quality score is lower than this threshold, the corresponding time series will be discarded.
`compute_future_values_input`	boolean	`False`: Compute future values of inputs. `True`: Select fields whose values you wish to add to the data.