PREDICT_AUTOREGRESSOR

Applies an autoregressor (AR) model to an input relation.

Applies an autoregressor (AR) model to an input relation.

Autoregressive models use previous values to make predictions. More specifically, the user-specified "lag" determines how many previous timesteps it takes into account during computation, and predicted values are linear combinations of those lags.

Syntax

PREDICT_AUTOREGRESSOR ( timeseries-column
        USING PARAMETERS
            model-name = 'model-name'
            [, start = starting-index]
            [, npredictions = npredictions]
            [, missing = "imputation-method" ] )
        OVER (ORDER BY timestamp-column)
        FROM input-relation

Arguments

timeseries-column
The timeseries column used to make the prediction (only the last p values, specified during model creation, are used).
timestamp-column
The timestamp column, with consistent timesteps, used to make the prediction.
input-relation
The input relation containing the timeseries-column and timestamp-column.

Note that input-relation cannot have missing values in any of the p (set during training) rows preceding start. To handle missing values, see IMPUTE or Linear interpolation.

Parameters

model_name

Name of the model (case-insensitive).

start
INTEGER >p or ≤0, the index (row) of the input-relation at which to start the prediction. If omitted, the prediction starts at the end of the input-relation.

If the start index is greater than the number of rows N in timeseries-column, then the values between N and start are predicted and used for the prediction.

If negative, the start index is identified by counting backwards from the end of the input-relation.

For an input-relation of N rows, negative values have a lower limit of either -1000 or -(N-p), whichever is greater.

Default: the end of input-relation

npredictions
INTEGER ≥1, the number of predicted timesteps.

Default: 10

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. If all values before or after a missing value in the prediction range are missing or invalid, interpolation is impossible and the function errors.

Default: linear_interpolation

Examples

See Autoregressive model example.

See also