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- AutoArima(data=None, data_filter_expr=None, max_pq_nonseasonal=[5, 5], max_pq_seasonal=[2, 2], start_pq_nonseasonal=[0, 0], start_pq_seasonal=[0, 0], d=-1, ds=-1, max_d=2, max_ds=1, period=1, stationary=False, seasonal=True, constant=True, algorithm='MLE', fit_percentage=100, infor_criteria='AIC', stepwise=False, nmodels=94, max_iterations=100, coeff_stats=False, fit_metrics=False, residuals=False, arma_roots=False, test_nonseasonal='ADF', test_seasonal='OCSB', output_fmt_index_style='NUMERICAL_SEQUENCE', **generic_arguments)
- DESCRIPTION:
AutoArima() function searches the possible models within the order
constrains in the function parameters, and returns the best ARIMA
model based on the criterion provided by the "infor_criteria"
parameter. AutoArima() function creates a six-layered ART table.
PARAMETERS:
data:
Required Argument.
Specifies the time series whose value can be REAL.
Types: TDSeries
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
max_pq_nonseasonal:
Optional Argument.
Specifies the (p,q) order of the maximum autoregression (AR) and
moving average (MA) parameters.
Default Value: [5,5]
Types: list
max_pq_seasonal:
Optional Argument.
Specifies the (P,Q) order of the max seasonal AR and MA
parameters.
Default Value: [2,2]
Types: list
start_pq_nonseasonal:
Optional Argument.
Specifies the start value of (p,q). Only used when "stepwise"=1.
Default Value: [0,0]
Types: list
start_pq_seasonal:
Optional Argument.
Specifies the start value of seasonal (P,Q). Only used when
"stepwise"=1.
Default Value: [0,0]
Types: list
d:
Optional Argument.
Specifies the order of first-differencing.
Default Value: -1 (auto search d).
Types: int
ds:
Optional Argument.
Specifies the order of seasonal-differencing.
Default Value: -1 (auto search Ds).
Types: int
max_d:
Optional Argument.
Specifies the maximum number of non-seasonal differences.
Default Value: 2
Types: int
max_ds:
Optional Argument.
Specifies the maximum number of seasonal differences.
Default Value: 1
Types: int
period:
Optional Argument.
Specifies the number of periods per season. For non-seasonal
data, period is 1.
Default Value: 1
Types: int
stationary:
Optional Argument.
Specifies whether to restrict search to stationary models.
If True, the function restricts search to stationary models.
Default Value: False
Types: bool
seasonal:
Optional Argument.
Specifies whether to restrict search to non-seasonal models.
If False, then the function restricts search to non-seasonal
models.
Default Value: True
Types: bool
constant:
Optional Argument.
Specifies whether an indicator that AutoArima() function includes
an intercept. If True, means CONSTANT/intercept
should be included. If False, means
CONSTANT/intercept should not be included.
Default Value: True
Types: bool
algorithm:
Optional Argument.
Specifies the approach used by TD_AUTOARIMA to estimate the
coefficients.
Permitted Values:
* MLE: Use maximum likelihood approach.
* CSS_MLE: Use the conditional sum-of-squares to determine a
start value and then do maximum likelihood.
* CSS: Use the conditional sum-of squares approach.
Default Value: MLE
Types: str
fit_percentage:
Optional Argument.
Specifies the percentage of passed-in sample points used for the
model fitting (parameter estimation).
Default Value: 100
Types: int
infor_criteria:
Optional Argument.
Specifies the information criterion to be used in model selection.
Permitted Values: AIC, AICC, BIC
Default Value: AIC
Types: str
stepwise:
Optional Argument.
Specifies whether the function does stepwise selection or not.
If True, then the function does stepwise selection otherwise the
function selects all models.
Default Value: False
Types: bool
nmodels:
Optional Argument.
Specifies the maximum number of models considered in the stepwise
search.
Default Value: 94
Types: int
max_iterations:
Optional Argument.
Specifies the maximum number of iterations that can be employed
to non-linear optimization procedure.
Default Value: 100
Types: int
coeff_stats:
Optional Argument.
Specifies the indicator to return coefficient statistical columns
TSTAT_VALUE and TSTAT_PROB. If True, means return
the columns otherwise do not return the
columns.
Default Value: False
Types: bool
fit_metrics:
Optional Argument.
Specifies the indicator to generate the secondary result set that
contains the model metadata statistics. If True,
means generate the secondary result set otherwise
do not generate the secondary result set.
Default Value: False
Types: bool
residuals:
Optional Argument.
Specifies the indicator to generate the tertiary result set that
contains the model residuals. If True, means
generate the tertiary result set, otherwise
do not generate the tertiary result set.
Default Value: False
Types: bool
arma_roots:
Optional Argument.
Specifies the indicator to generate the senary result set that
contains the inverse AR and MA roots of result best
model that AutoArima() selected (the model in the
primary output layer). There should be no inverse
roots showing outside of the unit circle. If True,
means generate result set otherwise do not
generate a result set.
Default Value: False
Types: bool
test_nonseasonal:
Optional Argument.
Specifies the nonseasonal unit root test used to choose
differencing number "d".
AutoArima() function only uses ADF test for
nonseasonal unit root test.
Permitted Values: ADF
Default Value: ADF
Types: str
test_seasonal:
Optional Argument.
Specifies the seasonal unit root test used to choose differencing
number "d". AutoArima() function only uses OCSB test for
seasonal unit root test.
Permitted Values: OCSB
Default Value: OCSB
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 AutoArima.
Output teradataml DataFrames can be accessed using attribute
references, such as AutoArima_obj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. result
2. fitmetadata
3. fitresiduals
4. model
5. icandorder
6. armaroots
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage, before importing the
# function in user space.
# 2. User can import the function, if it is available on
# Vantage user is connected to.
# 3. To check the list of UAF analytic functions available
# on Vantage user connected to, use
# "display_analytic_functions()".
# Check the list of available UAF analytic functions.
display_analytic_functions(type="UAF")
# Import function AutoArima.
from teradataml import AutoArima
# Load the example data.
load_example_data("uaf", ["blood2ageandweight", "covid_confirm_sd"])
# Create teradataml DataFrame object.
data = DataFrame.from_table("blood2ageandweight")
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=data,
id="PatientID",
row_index="SeqNo",
row_index_style="SEQUENCE",
payload_field="BloodFat",
payload_content="REAL")
# Example 1: Execute AutoArima with start_pq_nonseasonal as [1,1], algorithm = "MLE" and
# fit_percentage=80 to find the best ARIMA model.
uaf_out = AutoArima(data=data_series_df,
start_pq_nonseasonal=[1, 1],
seasonal=False,
constant=True,
algorithm="MLE",
fit_percentage=80,
stepwise=True,
nmodels=7,
fit_metrics=True,
residuals=True)
# Print the result DataFrames.
print(uaf_out.result)
# Example 2: Execute AutoArima with max_pq_nonseasonal as [3,3], arma_roots = True,
# to find thhe best ARIMA model.
covid_confirm_sd = DataFrame("covid_confirm_sd")
data_series_df = TDSeries(data=covid_confirm_sd,
id="city",
row_index="row_axis",
row_index_style="SEQUENCE",
payload_field="cnumber",
payload_content="REAL")
uaf_out = AutoArima(data=data_series_df,
max_pq_nonseasonal=[3, 3],
stationary=False,
stepwise=False,
arma_roots=True,
residuals=True)
# Print the result DataFrames.
print(uaf_out.result)
print(uaf_out.fitresiduals)
print(uaf_out.model)
print(uaf_out.icandorder)
print(uaf_out.armaroots)
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