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- UNDIFF(data1=None, data1_filter_expr=None, data2=None, data2_filter_expr=None, lag=None, differences=None, seasonal_multiplier=None, initial_values=None, input_fmt_input_mode=None, **generic_arguments)
- DESCRIPTION:
The UNDIFF() function is the reverse of the DIFF() function.
It takes in a previously differenced series processed by DIFF(),
and produces the original series that existed prior to the differencing.
PARAMETERS:
data1:
Required Argument.
Specifies the differenced series or TDAnalyticResult
object created on the output of DIFF() function.
Types: TDSeries, TDAnalyticResult
data1_filter_expr:
Optional Argument.
Specifies the filter expression for "data1".
Types: ColumnExpression
data2:
Optional Argument.
Specifies the original series.
This series is needed to reconstruct the series completely.
If the series was differenced with a lag of 1, then the initial
value of the original series must be present for a full
reconstruction. With a lag of 2, the initial 2 values must be present,
and so on. If the series was differenced multiple times, then the
initial values of the intermediate steps must be given.
Types: TDSeries
data2_filter_expr:
Optional Argument.
Specifies the filter expression for "data2".
Types: ColumnExpression
lag:
Required Argument.
Specifies the lag between series elements.
Value must be greater than or equal to 0.
Types: int
differences:
Required Argument.
Specifies the difference between time series elements Yt and Yt-lag.
Value must be greater than or equal to 0.
Types: int
seasonal_multiplier:
Required Argument.
Specifies whether time series is seasonal.
When set to 0, indicates time series is nonseasonal,
otherwise indicates seasonal time series.
Value must be greater than or equal to 0.
Types: int
initial_values:
Optional Argument.
Specifies the starting values for the undifferencing operation.
Types: float OR list of float
input_fmt_input_mode:
Optional Argument.
Specifies the input mode supported by the function.
Permitted Values: MANY2ONE, ONE2ONE, MATCH
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 UNDIFF.
Output teradataml DataFrames can be accessed using attribute
references, such as UNDIFF_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", ["ocean_buoy2"])
# Create teradataml DataFrame objects.
data = DataFrame.from_table("ocean_buoy2")
# Create teradataml TDSeries object.
data_series = TDSeries(data=data,
id="buoy_id",
row_index="n_seq_no",
row_index_style= "SEQUENCE",
payload_field="magnitude1",
payload_content="REAL")
# Transform time series into a differenced time series.
uaf_out_1 = DIFF(data=data_series,
lag=1,
differences=1,
seasonal_multiplier=0)
# Example 1 : Retrieve the original series that existed prior to the differencing
# by taking the differenced series processed by DIFF() as input.
# Create teradataml TDSeries over the output generated from DIFF.
data_series_1 = TDSeries(data=uaf_out_1.result,
id="buoy_id",
row_index="ROW_I",
row_index_style= "SEQUENCE",
payload_field="OUT_magnitude1",
payload_content="REAL")
uaf_out = UNDIFF(data1=data_series_1,
data2=data_series,
lag=1,
differences=1,
seasonal_multiplier=0,
input_fmt_input_mode="MATCH")
# Print the result DataFrame.
print(uaf_out.result)
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