The **Over time** chart helps you identify trends and potential gaps in your data by displaying, for both the original modeling data and the derived data, how a feature changes over the primary date/time feature. It is available for all time-aware projects (OTV, single series, and multiseries). For time series, it is available for each user-configured forecast distance.

Using the page's tools, you can focus on specific time periods. Display options for OTV and single-series projects differ from those of multiseries. Note that to view the **Over time** chart you must first compute chart data. Once computed:

1. Set the chart's granularity. The resolution options are auto-detected by DataRobot. All project types allow you to set a resolution (this option is under **Additional settings** for multiseries projects).

	![](images/fot-resolution.png)

2. Toggle the histogram display on and off to see a visualization of the bins DataRobot is using for [EDA1](eda-explained#eda1).

3. Use the date range slider below the chart to highlight a specific region of the time plot. For smaller datasets, you can drag the sliders to a selected portion. Larger data sets use block pagination.

	![](images/fot-slide.png)

4. For multiseries projects, you can set both the forecast distance and an individual series (or average across series) to plot:

	![](images/fot-resolution-multi.png)

For time series projects, the **Data** page also provides a [Feature Lineage](#feature-lineage-tab) chart to help understand the creation process for derived features.

## Partition without holdout {: #partition-without-holdout }

Sometimes, you may want to create a project without a holdout set, for example, if you have limited data points. Date/time partitioning projects have a minimum data ingest size of 140 rows. If **Add Holdout fold** is not checked, minimum ingest becomes 120 rows.

By default, DataRobot creates a holdout fold. When you toggle the switch off, the red holdout fold disappears from the representation (only the backtests and validation folds are displayed) and backtests recompute and shift to the right. Other configuration functionality remains the same&mdash;you can still modify the validation length and gap length, as well as the number of backtests. On the Leaderboard, after the project builds, you see validation and backtest scores, but no holdout score or **Unlock Holdout** option.

The following lists other differences when you do not create a holdout fold:

* Both the [**Lift Chart**](lift-chart#change-the-display) and [**ROC Curve**](pred-dist-graph#data-selection) can only be built using the validation set as their **Data Source**.
* The [**Model Info**](model-info) tab shows no holdout backtest and or warnings related to holdout.
* You can only compute predictions for **All data** and the **Validation** set from the [**Predict**](predict#why-use-training-data-for-predictions) tab.
* The [**Learning Curves**](learn-curve) graph does not plot any models trained into Validation or Holdout.
* [**Model Comparison**](model-compare) uses results only from validation and backtesting.

