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- SeasonalNormalize(data=None, data_filter_expr=None, season_cycle=None, cycle_duration=None, season_info=0, output_fmt_index_style='NUMERICAL_SEQUENCE', **generic_arguments)
- DESCRIPTION:
The function SeasonalNormalize() takes a non-stationary series and
normalizes the series by removing the unit roots. The function can
be used with any cyclic data that can be subdivided into a collection
of logical periods, in which each period can be further subdivided
into a collection of logical intervals.
The following procedure is an example of how to use SeasonalNormalize():
1. Detect the unit roots using DickeyFuller().
2. Use DIFF() or SeasonalNormalize() to eliminate unit roots.
3. Use Unnormalize() to undo the effects of SeasonalNormalize(),
and compare it to the original series.
PARAMETERS:
data:
Required Argument.
Specifies a logical-runtime series, with real number
elements.
Types: TDSeries
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
season_cycle:
Required Argument.
Specifies the logical time-unit.
Permitted Values:
* CAL_YEARS
* CAL_MONTHS
* CAL_DAYS
* WEEKS
* DAYS
* HOURS
* MINUTES
* SECONDS
* MILLISECONDS
* MICROSECONDS
Types: str
cycle_duration:
Required Argument.
Specifies the number of time-units as the duration of each seasonal
cycle.
Types: int
season_info:
Optional Argument.
Specifies whether to generate additional columns CYCLE_NO and
SEASON_NO. CYCLE_NO is the n-th cycle of the season. SEASON_NO
is the season for the data.
Permitted Values:
* 0 indicates no extra columns being generated.
* 1 indicates SEASON_NO column being generated.
* 2 indicates CYCLE_NO column being generated.
* 3 indicates both SEASON_NO and CYCLE_NO columns being generated.
Default Value: 0
Types: int
output_fmt_index_style:
Optional Argument.
Specifies the index style of the output format.
Default Value: NUMERICAL_SEQUENCE
Permitted Values:
* NUMERICAL_SEQUENCE
* FLOW_THROUGH
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 SeasonalNormalize.
Output teradataml DataFrames can be accessed using attribute
references, such as SeasonalNormalize_obj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. result
2. metadata
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", ["river_data"])
# Create teradataml DataFrame object.
data = DataFrame.from_table("river_data")
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=data,
id="station_id",
row_index="timevalue",
row_index_style="TIMECODE",
payload_field="flow_velocity",
payload_content="REAL",
interval="CAL_MONTHS(1)")
# Example 1 : Normalize the series by removing the unit roots.
uaf_out = SeasonalNormalize(data=data_series_df,
season_cycle="CAL_MONTHS",
cycle_duration=1)
# Print the result DataFrames.
print(uaf_out.result)
print(uaf_out.metadata)
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