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- MovingAverage(data=None, target_columns=None, alpha=0.1, start_rows=2, window_size=10, include_first=False, mavgtype='C', **generic_arguments)
- DESCRIPTION:
MovingAverage() function computes average values in a series, using the specified moving average type.
PARAMETERS:
data:
Required Argument.
Specifies the name of the teradataml DataFrame that contains the
columns.
Types: teradataml DataFrame
target_columns:
Optional Argument.
Specifies the input column names for which the moving average is to
be computed. If you omit this argument, then the function copies
every input column to the output teradataml DataFrame but does not
compute moving average.
Types: str OR list of Strings (str)
alpha:
Optional Argument.
Specifies the damping factor, a value in the range [0, 1], which
represents a percentage in the range [0, 100]. For example, if alpha
is 0.2, then the damping factor is 20%. A higher alpha discounts
older observations faster.
Default Value: 0.1
Types: float
start_rows:
Optional Argument.
Specifies the number of rows at the beginning of the time series that
the function "skips" before it begins the calculation of the
exponential moving average. The function uses the arithmetic average
of these rows as the initial value of the exponential moving average.
The value n must be an integer.
Default Value: 2
Types: int
window_size:
Optional Argument.
Specifies the number of previous values to include in the computation
of the simple moving average.
Default Value: 10
Types: int
include_first:
Optional Argument.
Specifies whether the first "start_rows" rows should be included in the
output or not.
Default Value: False
Types: bool
mavgtype:
Optional Argument.
Specifies the moving average type that needs to be used for computing
moving averages of TargetColumns.
Default Value: "C"
Permitted Values: C, S, M, W, E, T
Types: str
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in table or not.
When set to True, results are persisted in table; otherwise, results
are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in volatile table or not.
When set to True, results are stored in volatile table, otherwise not.
Default Value: False
Types: boolean
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying SQLE Engine
function supports, else an exception is raised.
RETURNS:
Instance of MovingAverage.
Output teradataml DataFrames can be accessed using attribute
references, such as MovingAverageObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
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 the Vantage user is connected to.
# 3. To check the list of analytic functions available on the Vantage user connected to,
# use "display_analytic_functions()"
# Load the example data.
load_example_data("movavg", ["ibm_stock"])
# Create teradataml DataFrame object.
ibm_stock = DataFrame.from_table("ibm_stock")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function MovingAverage.
from teradataml import MovingAverage
# Example 1: Compute the average values in a series, using the 'C' moving average type.
obj = MovingAverage(data=ibm_stock,
data_partition_column='stockprice',
data_order_column='stockprice',
include_first=False,
alpha=0.1,
start_rows=2,
window_size=10,
mavgtype='C')
# Print the result DataFrame.
print(obj.result)
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