PREDICT_MOVING_AVERAGE

Applies a moving-average (MA) model, created by MOVING_AVERAGE, to an input relation.

Applies a moving-average (MA) model, created by MOVING_AVERAGE, to an input relation.

Moving average models use the errors of previous predictions to make future predictions. More specifically, the user-specified "lag" determines how many previous predictions and errors it takes into account during computation.

Syntax

PREDICT_MOVING_AVERAGE ( '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 q 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 q (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 >q 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-q), 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 Moving-average model example.

See also