---
title: Time series (video)
description: Watch a series of videos to learn the essentials required to complete your first time series project in DataRobot.

---

# Time series videos {: #time-series-videos }

Learn the essentials required to complete your first time series project in DataRobot. Each video shows how to accomplish the necessary tasks using the DataRobot interface and the Python API in a DataRobot Notebook. Follow along with the tutorial by downloading the sample data set:

[Download Dataset <span style="vertical-align: sub">:material-download:{.lg }</span>](https://s3.amazonaws.com/datarobot_public_datasets/ai_accelerators/Car_Sales_Tutorial_SingleSeries_Multivariate.csv){ .md-button }

<hr>

## Data structures  {: #data-structures }                            

Learn about the data structures used with DataRobot's time series modeling functionality. The video shows how to structure your time series dataset for univariate, multivariate, and multi-series/multivariate problems&mdash;a critical step to shaping your data for building time series models.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Time Series Data Structures" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/nCMNjz60ThE" allowfullscreen></iframe>
</div>
<br>

<hr>

## Project settings {: #project-settings }

Learn how to start a time series project in DataRobot, configuring the initial project settings and modifying default configurations to suit your experimentation needs.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Time Series Project Settings" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/fFyKZ_PC8-s" allowfullscreen></iframe>
</div>
<br>

<hr>

## Feature engineering  {: #feature-engineering }

Learn to interpret and use DataRobot time series automation outputs, specifically features, feature lists, and Leaderboard models. This video illustrates the results of time series project automation, the key source of time savings for time series projects. Specifically, it shows you how to look at time series features and feature lists and determine how to organize and understand models on the Leaderboard.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Time Series Feature Engineering" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/1-uMObOzf34" allowfullscreen></iframe>
</div>
<br>

<hr>

## Model insights {: #model-insights }

Learn how to interpret the performance of a time series model and understand what patterns it has identified in your data. This video focuses on the insights and visualizations associated with each blueprint and each model on the Leaderboard. Specifically, it looks at the performance of a time series project and identifies important features in the top-performing model.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Time Series Model Insights" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/IUd89RpDZTM" allowfullscreen></iframe>
</div>
<br>

<hr>

## Model deployment {: #model-deployment }

Learn how to deploy a Leaderboard model. In this tutorial, you'll see the workflow for selecting any model on the Leaderboard and deploying it to a DataRobot Prediction Server.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Time Series Model Deployment" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/f3KBeTSaKwA" allowfullscreen></iframe>
</div>
<br>

<hr>

## Predictions {: #predictions }

In this tutorial, you'll learn how to use a production time series model for basic predictions. Importantly, you'll learn the correct structure for the prediction request file. Learning to make predictions with production models is critical to delivering business value.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Time Series Model Deployment" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/551TAcNCu0s" allowfullscreen></iframe>
</div>
<br>

## Read more {: #read-more }

* [Time series modeling index](time/index){ target=_blank }
* [Modify project settings (advanced)](ts-adv-opt){ target=_blank }
* [Time series modeling data](ts-modeling-data/index){ target=_blank }
* [Time series model insights](ts-leaderboard){ target=_blank }
* [Time series predictions](ts-predictions){ target=_blank }
* [External prediction comparison](cyob){ target=_blank }
