AUTOREGRESSOR
Creates an autoregressive (AR) model from a stationary time series with consistent timesteps that can then be used for prediction via PREDICT_AUTOREGRESSOR.
Autoregressive models predict future values of a time series based on the preceding values. More specifically, the userspecified lag determines how many previous timesteps it takes into account during computation, and predicted values are linear combinations of the values at each lag.
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
AUTOREGRESSOR ('modelname', 'inputrelation', 'datacolumn', 'timestampcolumn'
[ USING PARAMETERS
[ p = 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 represents the timestamp variable. Timesteps must be consistent.
Parameters
p
 INTEGER in the range [1, 1999], the number of lags to consider in the computation. Larger values for
p
weaken the correlation.Default: 3
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 linearlyinterpolated 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).
Default: False
Examples
See Autoregressive model example.