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- PACF(data=None, data_filter_expr=None, input_type=None, algorithm=None, max_lags=None, unbiased=False, alpha=None, **generic_arguments)
- DESCRIPTION:
The PACF() function provides insight as to whether the function
being modeled is stationary or not. The partial auto correlations
are used to measure the degree of correlation between series sample points.
The algorithm removes the effects of the previous lag. For example,
the coefficient for lag 4 focuses on the effect of activity based only
at lag 4, with effects of lags 3, 2, and 1 removed.
PARAMETERS:
data:
Required Argument.
Specifies a series or an analytical result that contains previously
computed auto correlation coefficients for lag and magnitude.
Types: TDSeries, TDAnalyticResult
data_filter_expr:
Optional Argument.
Specifies filter expression for "data".
Types: ColumnExpression
input_type:
Optional Argument.
Specifies the type of data in the series.
Permitted Values:
* DATA_SERIES: A one dimensional input array that contains
a time series or a spatial series.
* ACF: A one dimensional input array that is indexed by
LAG values, and contains previously-generated ACF magnitudes.
Types: str
algorithm:
Required Argument.
Specifies the algorithm to generate the partial auto-correlation function
"PACF" coefficients.
Permitted Values: LEVINSON_DURBIN, OLS
Types: str
max_lags:
Required Argument.
Specifies the maximum number of lags to calculate the partial autocorrelation.
The lag value is limited to one less than the number of observations in the series.
If the specified lag value exceeds the limit, the value is replaced with the
system-defined maximum value.
Default is 10*log10(N) where N is the number of observations.
Types: int
unbiased:
Optional Argument.
Specifies the formula to calculate the autocorrelation intermediate values.
When set to False, denominator for autocorrelation calculation uses the
Jenkins & Watts formula, otherwise uses the Box & Jenkins formula.
Note:
Only valid when "input_type" is 'DATA_SERIES'.
Default Value: False
Types: bool
alpha:
Optional Argument.
Specifies confidence intervals for the given level. For example, if 0.05 is entered,
then 95% confidence intervals are returned for standard deviation computed according
to Bartlett’s formula.
Types: float
**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 PACF.
Output teradataml DataFrames can be accessed using attribute
references, such as PACF_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", ["test_pacf_12"])
# Create teradataml DataFrame object.
df = DataFrame.from_table("test_pacf_12")
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=df,
id="buoy_id",
row_index="n_seq_no",
row_index_style="SEQUENCE",
payload_field="magnitude1",
payload_content="REAL")
# Example 1 : Calculate the partial autocorrelation function coefficients using
# 'LEVINSON_DURBIN' algorithm, with maximum of 10 lags.
PACF_out = PACF(data=data_series_df,
algorithm='LEVINSON_DURBIN',
max_lags=10)
# Print the result DataFrame.
print(PACF_out.result)
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