# Predictions with feature transformations
URL: https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-time-aware/ts-pred-transforms.html

> Describes how to create and manage time-aware experiments for large datasets using time-aware wrangling.

# Predictions with feature transformations

When making predictions with feature transformations, DataRobot assigns rows by forecast distance, builds separate models for each distance, and then makes row-by-row predictions. This method uses [time-aware wrangling](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/ts-wrangling.html) for transparent and flexible feature engineering.

## When to use

Use this method when:

- The dataset is larger than 10GB.
- Forecasting is not needed, but you want predictions based on forecast distance.
- You want full transparency of the transformation process.
- You want access to the repository of time series blueprints.

> [!NOTE] Note
> Predictions with feature transformations are only supported for regression experiments.

## How to use

To use this method:

1. Use time-aware wrangling to prepare your dataset.
2. Complete the partitioning part of the configuration described in the basic time-aware modeling setup.
3. Enable time series modeling and configure the parameters for feature derivation without the full time series feature derivation process.

```
graph TB
  A[Upload data] --> B["Configure wrangling (feature engineering)"]
  B --> C[Create experiment];
  C --> D[Enable date/time partitioning];
  D --> E[Set ordering feature];
  E -. optional .-> F[Set backtest partitions];
  F -. optional .-> G[Set sampling];
  G --> H[Enable time series modeling]
  H --> I[Disable automated feature engineering]
  I --> J[Set model selection criteria]
  J --> K[Start modeling]
```

## Enable time-aware predictions

Use any of the options below to access the toggle that allows you to create experiments that launch time-aware predictions or time series modeling:

- SelectGo to time series modeling settingsfrom theDate/timepartitioning setup page for time relevant data.
- SelectTime series modelingin theExperiment summarypanel.
- SelectTime series modelingin the top tabs.

All options open the settings to the Time series modeling tab. From there, toggle on Enable time series modeling.

## Disable feature engineering

Making predictions with time-aware data allows you to model on much larger datasets than those supported by traditional time series modeling. This is achieved by creating time-aware wrangling recipes and applying them to the data. The recipes allow you to customize the feature transformations to your needs, and because feature derivation has already been applied, you must disable the DataRobot automated process. To do so, toggle the option off.

## Set modeling parameters

Once feature engineering is disabled, additional settings become available. You must set at least one of the following to complete the configuration and begin modeling. Both forecasting distance and series ID define the criteria DataRobot uses to group rows, build models from those rows, and make predictions. Forecasting offsets set a value for DataRobot to add to the baseline model when building or scoring models.

- Forecasting distances
- Forecasting offsets
- Series ID (for multiseries projects)

Setting one or more of these parameters allows you to build time series blueprints. Without a "special" categorical column identified, DataRobot cannot use the time series blueprints and only those available to basic [date/time partitioned predictions](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-time-aware/ts-datetime.html) are available.

> [!NOTE] Note
> When selecting feature values for these parameters, DataRobot make all dataset features available. If you are unsure whether a specific feature is appropriate, visit the [dataset preview](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#__tabbed_1_2).

| Parameter | Description |
| --- | --- |
| Forecasting distances | Select a numeric feature that specifies the number of rows into the future each row should predict. The values in the feature you select will provide the distance offset for each row. If not set, DataRobot defaults to a uniform forecast distance of 1. Use time-aware wrangling to create a Forecast Distance column, as needed. |
| Forecasting offsets | Select one or more features that should be treated as a fixed component for modeling. Applying an offset to the baseline model during model training adds those values to raw predictions. The values must be pure numeric (currency, date, length, etc. are not acceptable). One model will be trained for each offset selected, and the selected column(s) must be present in any dataset later uploaded for predictions. |
| Series ID | Select a series identifier that groups rows by the series they belong to. |

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