??? note "Time series blueprints with Scoring Code support"

    <span id="ts-sc-blueprint-support">The following blueprints typically support Scoring Code:</span>

    * 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 support Scoring Code, there are situations when Scoring Code is unavailable:

    * Scoring Code might not be available for some models generated using [Feature Discovery](fd-time).
    * 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](glossary/index#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.
    * 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`.


??? note "Time series Scoring Code capabilities"

    The following capabilities are currently supported for time series Scoring Code:

    * [Time series parameters](sc-time-series#time-series-parameters-for-cli-scoring) for scoring at the command line.
    * [Segmented modeling](sc-time-series#scoring-code-for-segmented-modeling-projects)
    * [Prediction intervals](sc-time-series#prediction-intervals-in-scoring-code)
    * [Calendars](ts-adv-opt#calendar-files) (high resolution)
    * [Cross-series](ts-adv-opt#enable-cross-series-feature-generation)
    * [Zero inflated](ts-feature-lists#zero-inflated-models) / naïve binary
    * [Nowcasting](nowcasting) (historical range predictions)
    * ["Blind history" gaps](glossary/index#blind-history)
    * [Weighted features](ts-adv-opt#apply-weights)

    The following time series capabilities are not supported for Scoring Code:

    * Row-based / irregular data
    * Nowcasting (single forecast point)
    * Intramonth seasonality
    * Time series blenders
    * Autoexpansion
    * EWMA (Exponentially Weighted Moving Average)