Skip to content

🧪 ArimaRegressor

Auto Regressive Integrated Moving Average Model.

Examples:

pipeline example {
    // TODO
}
Stub code in ArimaRegressor.sdsstub 

14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
@Experimental
@PythonName("ArimaModelRegressor")
class ArimaRegressor() {
    /**
     * Whether the regressor is fitted.
     */
    @PythonName("is_fitted") attr isFitted: Boolean

    /**
     * Create a copy of this ARIMA Model and fit it with the given training data.
     *
     * This ARIMA Model is not modified.
     *
     * @param timeSeries The time series containing the target column, which will be used.
     *
     * @result fittedArima The fitted ARIMA Model.
     */
    @Pure
    @Category(DataScienceCategory.ModelingQClassicalRegression)
    fun fit(
        @PythonName("time_series") timeSeries: TimeSeriesDataset
    ) -> fittedArima: ArimaRegressor

    /**
     * Predict a target vector using a time series target column. The model has to be trained first.
     *
     * @param timeSeries The test dataset of the time series.
     *
     * @result prediction A timeseries containing the predicted target vector and a time dummy as time column.
     */
    @Pure
    @Category(DataScienceCategory.ModelingQClassicalRegression)
    fun predict(
        @PythonName("time_series") timeSeries: TimeSeriesDataset
    ) -> prediction: Table

    /**
     * Plot the predictions of the trained model to the given target of the time series. So you can see the predictions and the actual values in one plot.
     *
     * @param testSeries The time series containing the target vector.
     *
     * @result image Plots predictions of the given time series to the given target Column
     */
    @Pure
    @PythonName("plot_predictions")
    @Category(DataScienceCategory.ModelEvaluationQVisualization)
    fun plotPredictions(
        @PythonName("test_series") testSeries: TimeSeriesDataset
    ) -> image: Image
}

isFitted

Whether the regressor is fitted.

Type: Boolean

fit

Create a copy of this ARIMA Model and fit it with the given training data.

This ARIMA Model is not modified.

Parameters:

Name Type Description Default
timeSeries TimeSeriesDataset The time series containing the target column, which will be used. -

Results:

Name Type Description
fittedArima ArimaRegressor The fitted ARIMA Model.
Stub code in ArimaRegressor.sdsstub 

31
32
33
34
35
@Pure
@Category(DataScienceCategory.ModelingQClassicalRegression)
fun fit(
    @PythonName("time_series") timeSeries: TimeSeriesDataset
) -> fittedArima: ArimaRegressor

plotPredictions

Plot the predictions of the trained model to the given target of the time series. So you can see the predictions and the actual values in one plot.

Parameters:

Name Type Description Default
testSeries TimeSeriesDataset The time series containing the target vector. -

Results:

Name Type Description
image Image Plots predictions of the given time series to the given target Column
Stub code in ArimaRegressor.sdsstub 

57
58
59
60
61
62
@Pure
@PythonName("plot_predictions")
@Category(DataScienceCategory.ModelEvaluationQVisualization)
fun plotPredictions(
    @PythonName("test_series") testSeries: TimeSeriesDataset
) -> image: Image

predict

Predict a target vector using a time series target column. The model has to be trained first.

Parameters:

Name Type Description Default
timeSeries TimeSeriesDataset The test dataset of the time series. -

Results:

Name Type Description
prediction Table A timeseries containing the predicted target vector and a time dummy as time column.
Stub code in ArimaRegressor.sdsstub 

44
45
46
47
48
@Pure
@Category(DataScienceCategory.ModelingQClassicalRegression)
fun predict(
    @PythonName("time_series") timeSeries: TimeSeriesDataset
) -> prediction: Table