| Fields tab |
|
|
target |
field |
This is the target field. |
location |
field |
The location field for the model. Only geospatial fields are allowed. |
location_label
|
field |
The categorical field to be used in the output to label the locations chosen in
location |
time_field
|
field |
The time field for the model. Only fields with continuous measurement are allowed, and the
storage type must be time, date, timestamp, or integer. |
inputs |
[field1 ... fieldN]
|
A list of input fields. |
| Time Intervals tab |
|
|
interval_type_timestamp
|
Years
Quarters
Months
Weeks
Days
Hours
Minutes
Seconds
|
|
interval_type_date |
Years
Quarters
Months
Weeks
Days |
|
interval_type_time |
Hours
Minutes
Seconds
|
Limits the number of days per week that are taken into account when creating the time index
that STP uses for calculation |
interval_type_integer
|
Periods
(Time index fields only, Integer storage) |
The interval to which the data set will be converted. The selection available is dependent on
the storage type of the field that is chosen as the time_field for the
model. |
period_start
|
integer |
|
start_month
|
January
February
March
April
May
June
July
August
September
October
November
December |
The month the model will start to index from (for example, if set to March
but the first record in the data set is January, the model will skip the first two
records and start indexing at March. |
week_begins_on |
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday |
The starting point for the time index created by STP from the data |
days_per_week
|
integer |
Minimum 1, maximum 7, in increments of 1 |
hours_per_day
|
integer |
The number of hours the model accounts for in a day. If this is set to 10,
the model will start indexing at the day_begins_at time and continue indexing for
10 hours, then skip to the next value matching the day_begins_at value,
etc. |
day_begins_at
|
00:00
01:00
02:00
03:00
...
23:00
|
Sets the hour value that the model starts indexing from. |
interval_increment
|
1
2
3
4
5
6
10
12
15
20
30 |
This increment setting is for minutes or seconds. This determines where the model creates
indexes from the data. So with an increment of 30 and interval type
seconds, the model will create an index from the data every 30 seconds. |
data_matches_interval
|
Boolean |
If set to N, the conversion of the data to the regular
interval_type occurs before the model is built.
If your data is already in
the correct format, and the interval_type and any associated settings match your
data, set this to Y to prevent the conversion or aggregation of your
data.
Setting this to Y disables all of the Aggregation controls. |
agg_range_default
|
Sum
Mean
Min
Max
Median
1stQuartile
3rdQuartile |
This determines the default aggregation method used for continuous fields. Any continuous
fields which are not specifically included in the custom aggregation will be aggregated using the
method specified here. |
custom_agg
|
[[field, aggregation method],[]..]
Demo:
[['x5'
'FirstQuartile']['x4' 'Sum']] |
Structured property:
Script parameter: custom_agg
For example:
set :stpnode.custom_agg = [
[field1
function]
[field2 function]
]
Where
function is the aggregation function to be used with that field. |
| Basics tab |
|
|
include_intercept
|
flag |
|
max_autoregressive_lag
|
integer |
Minimum 1, maximum 5, in increments of 1. This is the
number of previous records required for a prediction. So if set to 5, for example,
then the previous 5 records are used to create a new forecast. The number of records specified here
from the build data are incorporated into the model and, therefore, the user does not need to
provide the data again when scoring the model. |
estimation_method
|
Parametric
Nonparametric
|
The method for modeling the spatial covariance matrix |
parametric_model
|
Gaussian
Exponential
PoweredExponential
|
Order parameter for Parametric spatial covariance model |
exponential_power
|
number |
Power level for PoweredExponential model. Minimum 1, maximum 2. |
| Advanced tab |
|
|
max_missing_values
|
integer |
The maximum percentage of records with missing values allowed in the model. |
significance
|
number |
The significance level for hypotheses testing in the model build. Specifies the significance
value for all the tests in STP model estimation, including two Goodness of Fit tests, effect
F-tests, and coefficient t-tests. |
| Output tab |
|
|
model_specifications
|
flag |
|
temporal_summary |
flag |
|
location_summary
|
flag |
Determines whether the Location Summary table is included in the model output. |
model_quality
|
flag |
|
test_mean_structure
|
flag |
|
mean_structure_coefficients
|
flag |
|
autoregressive_coefficients
|
flag |
|
test_decay_space
|
flag |
|
parametric_spatial_covariance
|
flag |
|
correlations_heat_map
|
flag |
|
correlations_map
|
flag |
|
location_clusters
|
flag |
|
similarity_threshold
|
number |
The threshold at which output clusters are considered similar enough to be merged into a
single cluster. |
max_number_clusters
|
integer |
The upper limit for the number of clusters which can be included in the model output. |
| Model Options tab |
|
|
use_model_name |
flag |
|
model_name |
string |
|
uncertainty_factor
|
number |
Minimum 0, maximum 100. Determines the increase in
uncertainty (error) applied to predictions in the future. It is the upper and lower bound for the
predictions. |
