MOVING_AVERAGE
Creates a movingaverage (MA) model from a stationary time series with consistent timesteps that can then be used for prediction via PREDICT_MOVING_AVERAGE.
Moving average models use the errors of previous predictions to make future predictions. More specifically, the userspecified lag determines how many previous predictions and errors it takes into account during computation.
Since its input data must be sorted by timestamp, this algorithm is singlethreaded.
This is a metafunction. You must call metafunctions in a toplevel SELECT statement.
Behavior type
VolatileSyntax
MOVING_AVERAGE ('modelname', 'inputrelation', 'datacolumn', 'timestampcolumn'
[ USING PARAMETERS
[ q = lags ]
[, missing = "imputationmethod" ]
[, regularization = "regularizationmethod" ]
[, lambda = regularizationvalue ]
[, compute_mse = boolean ]
] )
Arguments
modelname
 Identifies the model to create, where
modelname
conforms to conventions described in Identifiers. It must also be unique among all names of sequences, tables, projections, views, and models within the same schema. inputrelation
 The table or view containing the
timestampcolumn
.This algorithm expects a stationary time series as input; using a time series with a mean that shifts over time may lead to weaker results.
datacolumn
 An input column of type NUMERIC that contains the dependent variables or outcomes.
timestampcolumn
 One INTEGER, FLOAT, or TIMESTAMP column that represent the timestamp variable. Timesteps must be consistent.
Parameters
q
 INTEGER in the range [1, 67), the number of lags to consider in the computation.
Note
The MOVING_AVERAGE and ARIMA models use different training techniques that produce distinct models when trained with matching parameter values on the same data. For example, if you train a movingaverage model using the same data andq
value as an ARIMA model trained withp
andd
parameters set to zero, those two models will not be identical.Default: 1
missing
 One of the following methods for handling missing values:

drop: Missing values are ignored.

error: Missing values raise an error.

zero: Missing values are replaced with 0.

linear_interpolation: Missing values are replaced by linearly interpolated values based on the nearest valid entries before and after the missing value. This means that in cases where the first or last values in a dataset are missing, they will simply be dropped.
Default: linear_interpolation

regularization
 One of the following regularization methods used when fitting the data:

None

L2: weight regularization term which penalizes the squared weight value
Default: None

lambda
 FLOAT in the range [0, 100000], the regularization value, lambda.
Default: 1.0
compute_mse
 BOOLEAN, whether to calculate and output the mean squared error (MSE).
This parameter only accepts "true" or "false" rather than the standard literal equivalents for BOOLEANs like 1 or 0.
Default: False
Examples
See Movingaverage model example.