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- GenseriesFormula(data=None, data_filter_expr=None, formula=None, estimate_mode=False, output_fmt_index_style='NUMERICAL_SEQUENCE', **generic_arguments)
- DESCRIPTION:
The GenseriesFormula() function allows you to define and apply
a formula to generate a time series. This function has many use
cases, including the ability to generate a series that is
subtracted from a non-stationary series to make it stationary,
and to estimate beyond the sample data points.
The following procedure is an example of how to use the
GenseriesFormula() function:
* Determine that the series to be modeled includes
a trend.
* Use LinearRegr() function to remove the trend from
the series.
* Use the "fitmetadata" attribute from the function output,
to determine the trend by fitting the data set.
* Use GenseriesFormula() function to generate a
trend series.
* Use BinarySeriesOp() function to subtract the
generated trend from the original series.
PARAMETERS:
data:
Required Argument.
Specifies the input time series or self-generating
time series.
Notes:
* Series specification is a time series or
spatial series. Series can be REAL or MULTIVAR_REAL.
* Generated series specification uses a self-generated series.
Types: TDSeries, TDGenseries
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
formula:
Required Argument.
Specifies the formula to apply to the input time series.
The formula represents the trend or periodicity in the input time
series and conforms to formula rules.
If you specify generated series, TDGenSeries, the formula
can have only one explanatory variable. The function assigns
the starting value to the explanatory variable.
Note:
Use the following link to refer the formula rules in
Teradata document:
"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Mathematic-Operators-and-Functions/Formula-Rules"
Types: str
estimate_mode:
Optional Argument.
Specifies whether to include the input parameters in the results.
When set to True, function includes input parameters in the results,
otherwise does not include.
Default Value: False
Types: bool
output_fmt_index_style:
Optional Argument.
Specifies the index style of the output format.
Permitted Values: NUMERICAL_SEQUENCE, FLOW_THROUGH
Default Value: NUMERICAL_SEQUENCE
Types: str
**generic_arguments:
Specifies the generic keyword arguments of UAF functions.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Note that, when UAF function is executed, an
analytic result table (ART) is created.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile ART or not. When set to
True, results are stored in a volatile ART,
otherwise not.
Default Value: False
Types: bool
output_table_name:
Optional Argument.
Specifies the name of the table to store results.
If not specified, a unique table name is internally
generated.
Types: str
output_db_name:
Optional Argument.
Specifies the name of the database to create output
table into. If not specified, table is created into
database specified by the user at the time of context
creation or configuration parameter. Argument is ignored,
if "output_table_name" is not specified.
Types: str
RETURNS:
Instance of GenseriesFormula.
Output teradataml DataFrames can be accessed using attribute
references, such as GenseriesFormula_obj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Check the list of available UAF analytic functions.
display_analytic_functions(type="UAF")
from teradataml import FLOAT, INTEGER
# Load the example data.
load_example_data("uaf", ["production_data2"])
# Create teradataml DataFrame object.
data = DataFrame.from_table("production_data2")
# Example 1: Execute the GenseriesFormula() function on TDSeries input
# for index style 'FLOW_THROUGH' to generate a time series.
# New time series with the same type as the series
# specification payload content value.
# Create teradataml TDSeries object.
data_series_df_1 = TDSeries(data=data,
id="product_id",
row_index="TD_TIMECODE",
payload_field="beer_sales",
payload_content="REAL")
# Execute GenseriesFormula for TDSeries.
uaf_out_1 = GenseriesFormula(data=data_series_df_1,
formula="Y = 2.0*X1 + SIN(X1)",
output_fmt_index_style='FLOW_THROUGH')
# Print the result DataFrame.
print(uaf_out_1.result)
# Example 2: Execute the GenseriesFormula() function on TDGenSeries input
# for index style value 'NUMERICAL_SEQUENCE' and TDGenSeries
# data types as FLOAT to generate a time series.
# Functions returns new time series with the same type as
# the generated series specification payload content value.
# TDGenSeries datatypes is float.
# Create teradataml TDGenSeries object.
data_series_df_2 = TDGenSeries(instances = {"BuoyID": 3},
data_types = FLOAT(),
start=0.0,
offset=1.0,
num_entries=5)
# Execute GenseriesFormula for TDGenSeries.
uaf_out_2 = GenseriesFormula(data=data_series_df_2,
formula="Y = 3.0 + 8.0*X1",
output_fmt_index_style='NUMERICAL_SEQUENCE')
# Print the result DataFrame.
print(uaf_out_2.result)
# Example 3: Execute the GenseriesFormula() function on TDGenSeries input
# for index style value 'NUMERICAL_SEQUENCE' and TDGenSeries
# data types as INTEGER to generate a time series.
# Functions returns new time series with the same type as
# the generated series specification payload content value.
# TDGenSeries datatypes is integer.
# Create teradataml TDGenSeries object.
data_series_df_3 = TDGenSeries(instances = {"BuoyID": 3},
data_types = INTEGER(),
start=0,
offset=1,
num_entries=5)
# Execute GenseriesFormula for TDGenSeries.
uaf_out_3 = GenseriesFormula(data=data_series_df_3,
formula="Y = 3.0 + 8.0*X1",
output_fmt_index_style='NUMERICAL_SEQUENCE')
# Print the result DataFrame.
print(uaf_out_3.result)
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