# Scoring Code for time series projects
URL: https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-time-series.html

> How to use the Scoring Code feature for qualifying time series models, allowing you to use DataRobot-generated models outside of the DataRobot platform.

# Scoring Code for time series projects

[Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html) is a portable, low-latency method of utilizing DataRobot models outside of the DataRobot application. You can export time series models in a Java-based Scoring Code package from:

- TheLeaderboard: (Leaderboard > Predict > Portable Predictions)
- Adeployment: (Deployments > Predictions > Portable Predictions)

> [!NOTE] Time series Scoring Code considerations
> For information on the time series projects, models, and capabilities with Scoring Code support, see the [Time series support](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-time-series.html#time-series-support) section.

## Time series parameters for CLI scoring

DataRobot supports using [scoring at the command line](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/scoring-cli.html). The following table describes the time series parameters:

| Field | Required? | Default | Description |
| --- | --- | --- | --- |
| --forecast_point=<value> | No | None | Formatted date from which to forecast. |
| --date_format=<value> | No | None | Date format to use for output. |
| --predictions_start_date=<value> | No | None | Timestamp that indicates when to start calculating predictions. |
| --predictions_end_date=<value> | No | None | Timestamp that indicates when to stop calculating predictions. |
| --with_intervals | No | None | Turns on prediction interval calculations. |
| --interval_length=<value> | No | None | Interval length as int value from 1 to 99. |
| --time_series_batch_processing | No | Disabled | Enables performance-optimized batch processing for time-series models. |

## Scoring Code for segmented modeling projects

With [segmented modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-segmented.html), you can build individual models for segments of a multiseries project. DataRobot then merges these models into a Combined Model.

> [!NOTE] Note
> Scoring Code support is available for segments defined by an ID column in the dataset, not segments discovered by a clustering model.

### Verify that segment models have Scoring Code

If the champion model for a segment does not have Scoring Code, select a model that does have Scoring Code:

1. Navigate to the Combined Model on the Leaderboard.
2. From theSegmentdropdown menu, select a segment. Locate the champion for the segment (designated by the SEGMENT CHAMPIONindicator).
3. If the segment champion does not have a SCORING CODE indicator, select a new model that meets your modeling requirements and has the SCORING CODE indicator. Then selectLeaderboard options > Mark Model as Championfrom theMenuat the top. The segment now has a segment champion with Scoring Code:
4. Repeat the process for each segment of the Combined Model to ensure that all of the segment champions have Scoring Code.

### Download Scoring Code for a Combined Model

To download the Scoring Code JAR for a Combined Model:

- From the leaderboard:Download the Scoring Codefrom the Combined Model.
- From a deployment:Deploy your Combined Model, ensure thateach segment has Scoring Code, anddownload the Scoring Codefrom the Combined Model deployment.

## Prediction intervals in Scoring Code

You can now include prediction intervals in the downloaded Scoring Code JAR for a time series model. Supported intervals are 1 to 99.

### Download Scoring Code with prediction intervals

To download the Scoring Code JAR with prediction intervals enabled:

- From the leaderboard:Download the Scoring CodewithInclude Prediction Intervalsenabled.
- From a deployment:Deploy your modelanddownload the Scoring CodewithInclude Prediction Intervalsenabled.

### CLI example using prediction intervals

The following is a CLI example for scoring models using prediction intervals:

```
java -jar model.jar csv \
    --input=syph.csv \
    --output=output.csv \
    --with_intervals \
    --interval_length=87
```

## Feature considerations

Consider the following when working with Scoring Code:

- Using Scoring Code in production requires additional development efforts to implement model management and model monitoring, which the DataRobot API provides out of the box.
- Exportable Java Scoring Code requires extra RAM during model building. As a result, to use this feature, you should keep your training dataset under 8GB. Projects larger than 8GB may fail due to memory issues. If you get an out-of-memory error, decrease the sample size and try again. The memory requirementdoes not apply during model scoring. During scoring, the only limitation on the dataset is the RAM of the machine on which the Scoring Code is run.

### Model support

Consider the following model support considerations when planning to use Scoring Code:

- Scoring Code is available for models containing onlysupportedbuilt-in tasks. It is not available forcustom modelsor models containing one or morecustom tasks.
- Scoring Code is not supported inmultilabelprojects.
- Keras models do not support Scoring Code by default; however, support can be enabled by having an administrator activate the Enable Scoring Code Support for Keras Models feature flag. Note that these models are not compatible with Scoring Code for Android and Snowflake.

Additional instances in which Scoring Code generation is not available include:

- Naive Bayes models
- Visual AI and Location AI models
- Text tokenization involving the MeCab tokenizer for Japanese text (accessed via Advanced Tuning )

> [!NOTE] Text tokenization
> Using the default text tokenization configuration, char-grams, Japanese text is supported.

### Time series support

The following time series projects and models don't support Scoring Code:

- Time series binary classification projects
- Time series feature derivation projects resulting in datasets larger than 5GB
- Time series anomaly detection models

> [!NOTE] Anomaly detection models support
> While time series anomaly detection models don't generally support Scoring Code, it is supported for IsolationForest and some XGBoost-based anomaly detection model blueprints. For a list of supported time series blueprints, see [Time series blueprints with Scoring Code support](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-time-series.html#time-series-blueprints-with-scoring-code-support).

#### Unsupported capabilities

The following capabilities are not supported for Scoring Code. If Scoring Code is not generated due to an unsupported task in the blueprint, the reason is shown in the Details > Log tab.

- Row-based / irregular data
- Nowcasting (single forecast point)
- Intramonth seasonality
- Time series blenders
- Autoexpansion
- Exponentially Weighted Moving Average (EWMA)
- Clustering
- Partial history / cold start
- Prediction Explanations
- Type conversions after uploading data

#### Supported capabilities

The following capabilities are supported for time series Scoring Code:

- Time series parameters for scoring at the command line
- Segmented modeling
- Prediction intervals
- Calendars (high resolution)
- Cross-series
- Zero inflated / naïve binary
- Nowcasting (historical range predictions)
- "Blind history" gaps
- Weighted features

> [!NOTE] Weighted features support
> While weighted features are generally supported, they can result in Scoring Code becoming unavailable due to validation issues; for example, differences in rolling sum computation can cause consistency issues in projects with a weight feature and models trained on feature lists with `weighted std` or `weighted mean`.

#### Time series blueprints with Scoring Code support

The following blueprints typically support Scoring Code:

- AUTOARIMA with Fixed Error Terms
- ElasticNet Regressor (L2 / Gamma Deviance) using Linearly Decaying Weights with Forecast Distance Modeling
- ElasticNet Regressor (L2 / Gamma Deviance) with Forecast Distance Modeling
- ElasticNet Regressor (L2 / Poisson Deviance) using Linearly Decaying Weights with Forecast Distance Modeling
- ElasticNet Regressor (L2 / Poisson Deviance) with Forecast Distance Modeling
- Eureqa Generalized Additive Model (250 Generations)
- Eureqa Generalized Additive Model (250 Generations) (Gamma Loss)
- Eureqa Generalized Additive Model (250 Generations) (Poisson Loss)
- Eureqa Regressor (Quick Search: 250 Generations)
- eXtreme Gradient Boosted Trees Regressor
- eXtreme Gradient Boosted Trees Regressor (Gamma Loss)
- eXtreme Gradient Boosted Trees Regressor (Poisson Loss)
- eXtreme Gradient Boosted Trees Regressor with Early Stopping
- eXtreme Gradient Boosted Trees Regressor with Early Stopping (Fast Feature Binning)
- eXtreme Gradient Boosted Trees Regressor with Early Stopping (Gamma Loss)
- eXtreme Gradient Boosted Trees Regressor with Early Stopping (learning rate =0.06) (Fast Feature Binning)
- eXtreme Gradient Boosting on ElasticNet Predictions
- eXtreme Gradient Boosting on ElasticNet Predictions (Poisson Loss)
- Light Gradient Boosting on ElasticNet Predictions
- Light Gradient Boosting on ElasticNet Predictions (Gamma Loss)
- Light Gradient Boosting on ElasticNet Predictions (Poisson Loss)
- Performance Clustered Elastic Net Regressor with Forecast Distance Modeling
- Performance Clustered eXtreme Gradient Boosting on Elastic Net Predictions
- RandomForest Regressor
- Ridge Regressor using Linearly Decaying Weights with Forecast Distance Modeling
- Ridge Regressor with Forecast Distance Modeling
- Vector Autoregressive Model (VAR) with Fixed Error Terms
- IsolationForest Anomaly Detection with Calibration (time series)
- Anomaly Detection with Supervised Learning (XGB) and Calibration (time series)

While the blueprints listed above typically support Scoring Code, there are situations when Scoring Code is unavailable:

- Scoring Code might not be available for some models generated using Feature Discovery .
- Consistency issues can occur for non day-level calendars when the event is not in the dataset; therefore, Scoring Code is unavailable.
- Consistency issues can occur when inferring the forecast point in situations with a non-zero blind history ; however, Scoring Code is still available in this scenario.
- Scoring Code might not be available for some models that use text tokenization involving the MeCab tokenizer for Japanese text (accessed via Advanced Tuning ). Using the default configuration of char-grams during AutoPilot, Japanese text is supported.
- Differences in rolling sum computation can cause consistency issues in projects with a weight feature and models trained on feature lists with weighted std or weighted mean .

### Prediction Explanations support

Consider the following when working with Prediction Explanations for Scoring Code:

- To download Prediction Explanations with Scoring Code, youmustselectInclude Prediction ExplanationsduringLeaderboard downloadorDeployment download. This option isnotavailable forLegacy download.
- Scoring CodeonlysupportsXEMP-basedPrediction Explanations.SHAP-basedPrediction Explanations aren't supported.
- Scoring Codedoesn'tsupport Prediction Explanations for time series models.

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