ts properties

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. This Time Series node is similar to the previous Time Series node that was deprecated in SPSS® Modeler version 18. However, this newer Time Series node is designed to harness the power of IBM® SPSS Analytic Server to process big data, and display the resulting model in the output viewer that was added in SPSS Modeler version 17.

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 the topic 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.