## Build time-aware models {: #build-time-aware-models }

Once you click **Start**, DataRobot begins the model-building process and returns results to the Leaderboard. Because time series modeling uses date/time partitioning, you can run backtests, change window sampling, change training periods, and more from the Leaderboard.

!!! note
    Model parameter selection has not been customized for date/time-partitioned projects. Though automatic parameter selection yields good results in most cases, [**Advanced Tuning** ](adv-tuning) may significantly improve performance for some projects that use the Date/Time partitioning feature.


### Date duration features {: #date-duration-features }

Because having raw dates in modeling can be risky (overfitting, for example, or tree-based models that do not extrapolate well), DataRobot generally excludes them from the Informative Features list if date transformation features were derived. Instead, for OTV projects, DataRobot creates duration features calculated from the difference between date features and the primary date. It then adds the duration features to an optimized Informative Features list. The automation process creates:

* New duration features
* New feature lists

#### New duration features {: #new-duration-features }

When derived features (hour of day, day of week, etc.) are created, the feature type of the newly derived features are not dates. Instead, they become categorical or numeric, for example. To ensure that models learn time distances better, DataRobot computes the duration between primary and non-primary dates, adds that calculation as a feature, and then drops all non-primary dates.

Specifically, when date derivations happen in an OTV project, DataRobot creates one or more new features calculated from the duration between dates. The new features are named `duration(<from date>, <to date>)`, where the `<from date>` is the primary date. The var type, displayed on the **Data** page, displays `Date Duration`.

![](images/date-duration-dp.png)


The transformation applies even if the time units differ. In that case, DataRobot computes durations in seconds and displays the information on the **Data** page (potentially as huge integers). In some cases, the value is negative because the `<to date>` may be before the primary date.


#### New feature lists {: #new-feature-lists }

The new feature lists, automatically created based on Informative Features and Raw Features, are a copy of the originals with the duration feature(s) added. They are named the same, but with "optimized for time-aware modeling" appended. (For univariate feature lists, `duration` features are only added if the original date feature was part of the original univariate list.)

![](images/date-duration-fls.png)

When you run full or Quick Autopilot, new feature lists are created later in the [EDA2](eda-explained#eda2) process. DataRobot then switches the Autopilot process to use the new, optimized list. To use one of the non-optimized lists, you must rerun Autopilot specifying the list you want.
