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- Unnormalize(data1=None, data1_filter_expr=None, data2=None, data2_filter_expr=None, fields=None, input_fmt_input_mode=None, output_fmt_index_style='NUMERICAL_SEQUENCE', **generic_arguments)
- DESCRIPTION:
The Unnormalize() function reconstructs a series created by SeasonalNormalize().
The function is usually used for the forecasting phase of modeling.
The following procedure is an example of how to use function when the series
to be modeled is found to be unstationary:
* Use SeasonalNormalize() to make the series stationary.
* Develop the ARIMA forecast model.
* Use the ARIMA model to forecast the normalized series.
* Use function on the forecasted normalized series to undo the
effects of normalization and produce the final forecasted series result.
PARAMETERS:
data1:
Required Argument.
Specifies the input series to unnormalize.
Input payload content type can be REAL, MUTLIVAR_REAL, or
MUTLTIVAR_ANYTYPE. TDSeries must include "interval" argument.
Types: TDSeries
data1_filter_expr:
Optional Argument.
Specifies the filter expression for "data1".
Types: ColumnExpression
data2:
Required Argument.
Specifies the output series. Output series type matches input series type.
This should be a metadata output of SeasonalNormalize() function or
TDAnalyticResults created on the teradataml dataframe that contains
previously normalized results with the (mean, standardDeviation) pairs
needed to unnormalize the first input series.
Types: TDSeries, TDAnalyticResult
data2_filter_expr:
Optional Argument.
Specifies the filter expression for "data2".
Types: ColumnExpression
fields:
Optional Argument.
Specifies fields to unnormalize. The fields should be separated by commas.
When specified, only the specified payload fields in the list are unnormalized,
otherwise all payload fields of the first input are unnormalized.
Values should be 0 or positive numbers. The payload field indices start
at 1.
Types: int
input_fmt_input_mode:
Required Argument.
Specifies the input mode supported by the function.
Permitted Values: MANY2ONE, ONE2ONE, MATCH
Types: str
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 Unnormalize.
Output teradataml DataFrames can be accessed using attribute
references, such as Unnormalize_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")
# Load the example data.
load_example_data("uaf", ["store_sales"])
# Create teradataml DataFrame objects.
df = DataFrame("store_sales")
# Create teradataml TDSeries objects.
td_series = TDSeries(data=df,
row_index="ts",
row_index_style="TIMECODE",
id="StoreID",
payload_field="Sales",
payload_content="REAL",
interval="CAL_MONTHS(1)"
)
# Produce the stationary series.
seasonalnormalize = SeasonalNormalize(data=td_series,
season_cycle="CAL_YEARS",
cycle_duration=1,
output_fmt_index_style="FLOW_THROUGH"
)
# Create teradataml TDSeries objects.
td_series1 = TDSeries(data=seasonalnormalize.result,
id="StoreID",
row_index="ROW_I",
row_index_style="TIMECODE",
payload_field="Sales",
payload_content="REAL",
interval="CAL_MONTHS(1)"
)
# Example 1 : Function reverse the effects of normalization and
# produce the final forecasted series result using TDSeries.
# Create teradataml TDSeries objects.
td_series2 = TDSeries(data=seasonalnormalize.metadata,
id="StoreID",
row_index="ROW_I",
row_index_style="SEQUENCE",
payload_field=["MEAN_Sales", "SD_Sales"],
payload_content="MULTIVAR_REAL"
)
uaf_out = Unnormalize(data1=td_series1,
data2=td_series2,
input_fmt_input_mode="MATCH",
output_fmt_index_style="FLOW_THROUGH")
# Print the result DataFrame.
print(uaf_out.result)
# Example 2 : Function reverse the effects of normalization and
# produce the final forecasted series result using TDAnalyticResult.
# Create teradataml TDAnalyticResult objects.
td_art = TDAnalyticResult(data=seasonalnormalize.result,
layer='ARTMETADATA')
uaf_out = Unnormalize(data1=td_series1,
data2=td_art,
input_fmt_input_mode="MATCH",
output_fmt_index_style="FLOW_THROUGH")
# Print the result DataFrame.
print(uaf_out.result)
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