ts properties

Time Series node iconThe 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 that 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.